{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3696c1f6-d12b-4059-9f82-857cb66fe584",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import patsy\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c38270-3e8b-45eb-b1d3-00698c0edea0",
   "metadata": {},
   "source": [
    "# Figure 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "65cad0e1-9f97-4b10-80e3-8ab112065a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Low</th>\n",
       "      <th>High</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.524</td>\n",
       "      <td>4.438</td>\n",
       "      <td>4.610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>3.743</td>\n",
       "      <td>3.659</td>\n",
       "      <td>3.828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.401</td>\n",
       "      <td>4.315</td>\n",
       "      <td>4.486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>3.190</td>\n",
       "      <td>3.106</td>\n",
       "      <td>3.275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Wealth            Frame   Mean    Low   High\n",
       "0    Low             Risk  4.524  4.438  4.610\n",
       "1    Low  Competitiveness  3.743  3.659  3.828\n",
       "2   High             Risk  4.401  4.315  4.486\n",
       "3   High  Competitiveness  3.190  3.106  3.275"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2220x1380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved:\n",
      "E:\\불평등 연구\\데이터\\73_AI_성장\\figure_1_policy_priority.png\n",
      "E:\\불평등 연구\\데이터\\73_AI_성장\\figure_1_policy_priority.pdf\n"
     ]
    }
   ],
   "source": [
    "# ----------------------------\n",
    "# Paths\n",
    "# ----------------------------\n",
    "base_dir = r\"\"\n",
    "file_name = \"df_1.xlsx\"\n",
    "data_path = os.path.join(base_dir, file_name)\n",
    "\n",
    "out_png = os.path.join(base_dir, \"figure_1_policy_priority.png\")\n",
    "out_pdf = os.path.join(base_dir, \"figure_1_policy_priority.pdf\")\n",
    "\n",
    "# ----------------------------\n",
    "# Load data\n",
    "# ----------------------------\n",
    "df = pd.read_excel(data_path)\n",
    "\n",
    "if \"attention_check_pass\" in df.columns:\n",
    "    df = df[df[\"attention_check_pass\"] == 1].copy()\n",
    "\n",
    "df = df[df[\"wealth_tertile\"].isin([\"Low\", \"High\"])].copy()\n",
    "df[\"wealth_LH\"] = df[\"wealth_tertile\"]\n",
    "\n",
    "frame_order = [\"Risk\", \"Competitiveness\"]\n",
    "wealth_order = [\"Low\", \"High\"]\n",
    "\n",
    "df[\"frame\"] = pd.Categorical(df[\"frame\"], categories=frame_order, ordered=True)\n",
    "df[\"wealth_LH\"] = pd.Categorical(df[\"wealth_LH\"], categories=wealth_order, ordered=True)\n",
    "\n",
    "# ----------------------------\n",
    "# Model\n",
    "# ----------------------------\n",
    "formula = \"\"\"\n",
    "policy_priority\n",
    "~ C(frame) * C(wealth_LH)\n",
    "+ C(context)\n",
    "+ age\n",
    "+ C(gender)\n",
    "+ C(region)\n",
    "+ C(education)\n",
    "+ C(income_category)\n",
    "+ C(employment_status)\n",
    "+ C(occupation_group)\n",
    "+ C(union_membership)\n",
    "+ C(marital_status)\n",
    "+ political_ideology_0_10\n",
    "\"\"\"\n",
    "\n",
    "model = smf.ols(formula=formula, data=df).fit(cov_type=\"HC3\")\n",
    "\n",
    "# ----------------------------\n",
    "# Marginal Predictions\n",
    "# ----------------------------\n",
    "rng = np.random.default_rng(20260228)\n",
    "B = 2000\n",
    "\n",
    "beta_hat = model.params.to_numpy()\n",
    "V = model.cov_params().to_numpy()\n",
    "beta_draws = rng.multivariate_normal(mean=beta_hat, cov=V, size=B)\n",
    "\n",
    "design_info = model.model.data.design_info\n",
    "\n",
    "def marginal_pred_ci(frame_value, wealth_value):\n",
    "    sub = df[df[\"wealth_LH\"] == wealth_value].copy()\n",
    "    cf = sub.copy()\n",
    "    cf[\"frame\"] = frame_value\n",
    "    cf[\"frame\"] = pd.Categorical(cf[\"frame\"], categories=frame_order, ordered=True)\n",
    "\n",
    "    X_cf = patsy.dmatrix(design_info, cf, return_type=\"dataframe\").to_numpy()\n",
    "\n",
    "    mean_point = (X_cf @ beta_hat).mean()\n",
    "    sim_means = (X_cf @ beta_draws.T).mean(axis=0)\n",
    "    lo, hi = np.quantile(sim_means, [0.025, 0.975])\n",
    "    return mean_point, lo, hi\n",
    "\n",
    "rows = []\n",
    "for w in wealth_order:\n",
    "    for f in frame_order:\n",
    "        m, lo, hi = marginal_pred_ci(f, w)\n",
    "        rows.append({\"Wealth\": w, \"Frame\": f, \"Mean\": m, \"Low\": lo, \"High\": hi})\n",
    "\n",
    "pred_df = pd.DataFrame(rows)\n",
    "display(pred_df.round(3))\n",
    "\n",
    "# ----------------------------\n",
    "# Plot\n",
    "# ----------------------------\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11,\n",
    "    \"axes.labelsize\": 11,\n",
    "    \"xtick.labelsize\": 11,\n",
    "    \"ytick.labelsize\": 11,\n",
    "    \"legend.fontsize\": 11,\n",
    "})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.4, 4.6), dpi=300)\n",
    "\n",
    "x = np.arange(len(frame_order))\n",
    "\n",
    "colors = {\n",
    "    \"Low\":  \"#8B0000\",   # Dark Red\n",
    "    \"High\": \"#003366\"    # Dark Blue\n",
    "}\n",
    "\n",
    "linestyles = {\n",
    "    \"Low\": \"-\",\n",
    "    \"High\": \"--\"\n",
    "}\n",
    "\n",
    "markers = {\n",
    "    \"Low\": \"o\",\n",
    "    \"High\": \"s\"\n",
    "}\n",
    "\n",
    "for w in wealth_order:\n",
    "    sub = pred_df[pred_df[\"Wealth\"] == w].set_index(\"Frame\").loc[frame_order]\n",
    "    y = sub[\"Mean\"].values\n",
    "    yerr = np.vstack([y - sub[\"Low\"].values, sub[\"High\"].values - y])\n",
    "\n",
    "    ax.errorbar(\n",
    "        x, y, yerr=yerr,\n",
    "        capsize=4,\n",
    "        elinewidth=1.2,\n",
    "        linewidth=2.2,\n",
    "        linestyle=linestyles[w],\n",
    "        marker=markers[w],\n",
    "        markersize=6.5,\n",
    "        color=colors[w],\n",
    "        label=f\"Wealth: {w}\"\n",
    "    )\n",
    "\n",
    "    # ---- Annotate predicted values ----\n",
    "    for xi, yi in zip(x, y):\n",
    "        if w == \"Low\":        # Red → above marker\n",
    "            offset = 0.22\n",
    "            va = \"bottom\"\n",
    "        else:                 # Blue → below marker\n",
    "            offset = -0.22\n",
    "            va = \"top\"\n",
    "\n",
    "        ax.text(\n",
    "            xi,\n",
    "            yi + offset,\n",
    "            f\"{yi:.2f}\",\n",
    "            color=colors[w],\n",
    "            fontsize=10,\n",
    "            ha=\"center\",\n",
    "            va=va\n",
    "        )\n",
    "\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels([\"Risk frame\", \"Competitiveness frame\"])\n",
    "ax.set_ylabel(\"Policy Priority (1=Industrial support, 7=Regulation)\")\n",
    "\n",
    "ax.set_ylim(1, 7)\n",
    "ax.set_yticks(np.arange(1, 8, 1))\n",
    "\n",
    "# 4-side border\n",
    "for spine in [\"top\", \"right\", \"left\", \"bottom\"]:\n",
    "    ax.spines[spine].set_visible(True)\n",
    "    ax.spines[spine].set_linewidth(1.0)\n",
    "\n",
    "ax.legend(loc=\"upper left\", frameon=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(left=0.13)\n",
    "\n",
    "fig.savefig(out_png, bbox_inches=\"tight\")\n",
    "fig.savefig(out_pdf, bbox_inches=\"tight\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(\"Saved:\")\n",
    "print(out_png)\n",
    "print(out_pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b9879ed-0245-41b0-be7c-ea87d034b608",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f3773b6e-4357-45d3-9ccd-1463f37ee0b9",
   "metadata": {},
   "source": [
    "# Figure 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5554f718-fc48-40e6-8362-567f8be579dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4bf174a1-f733-4d58-97fe-df09f56c62d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicted values: Industrial Policy Support (marginal standardized, HC3 simulation CI)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Mean</th>\n",
       "      <th>CI 2.5%</th>\n",
       "      <th>CI 97.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>5.509</td>\n",
       "      <td>5.423</td>\n",
       "      <td>5.596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.478</td>\n",
       "      <td>4.385</td>\n",
       "      <td>4.568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>5.079</td>\n",
       "      <td>4.997</td>\n",
       "      <td>5.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.477</td>\n",
       "      <td>4.391</td>\n",
       "      <td>4.565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Wealth            Frame   Mean  CI 2.5%  CI 97.5%\n",
       "3   High  Competitiveness  5.509    5.423     5.596\n",
       "2   High             Risk  4.478    4.385     4.568\n",
       "1    Low  Competitiveness  5.079    4.997     5.164\n",
       "0    Low             Risk  4.477    4.391     4.565"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicted values: Regulation Support (marginal standardized, HC3 simulation CI)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Mean</th>\n",
       "      <th>CI 2.5%</th>\n",
       "      <th>CI 97.5%</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>4.543</td>\n",
       "      <td>4.449</td>\n",
       "      <td>4.632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.931</td>\n",
       "      <td>4.847</td>\n",
       "      <td>5.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>4.514</td>\n",
       "      <td>4.426</td>\n",
       "      <td>4.600</td>\n",
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       "      <th>0</th>\n",
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       "      <td>5.436</td>\n",
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       "  Wealth            Frame   Mean  CI 2.5%  CI 97.5%\n",
       "3   High  Competitiveness  4.543    4.449     4.632\n",
       "2   High             Risk  4.931    4.847     5.018\n",
       "1    Low  Competitiveness  4.514    4.426     4.600\n",
       "0    Low             Risk  5.351    5.265     5.436"
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",
      "text/plain": [
       "<Figure size 3300x1380 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved:\n",
      "E:\\불평등 연구\\데이터\\73_AI_성장\\figure_2_industrial_regulation.png\n",
      "E:\\불평등 연구\\데이터\\73_AI_성장\\figure_2_industrial_regulation.pdf\n"
     ]
    }
   ],
   "source": [
    "# ----------------------------\n",
    "# Paths\n",
    "# ----------------------------\n",
    "base_dir = r\"E:\\\"\n",
    "file_name = \"df_1.xlsx\"\n",
    "data_path = os.path.join(base_dir, file_name)\n",
    "\n",
    "out_png = os.path.join(base_dir, \"figure_2_industrial_regulation.png\")\n",
    "out_pdf = os.path.join(base_dir, \"figure_2_industrial_regulation.pdf\")\n",
    "\n",
    "# ----------------------------\n",
    "# Load\n",
    "# ----------------------------\n",
    "df = pd.read_excel(data_path)\n",
    "\n",
    "# Pre-registered exclusion\n",
    "if \"attention_check_pass\" in df.columns:\n",
    "    df = df[df[\"attention_check_pass\"] == 1].copy()\n",
    "\n",
    "# Main-text contrast: Low vs High only\n",
    "df = df[df[\"wealth_tertile\"].isin([\"Low\", \"High\"])].copy()\n",
    "df[\"wealth_LH\"] = df[\"wealth_tertile\"]\n",
    "\n",
    "frame_order = [\"Risk\", \"Competitiveness\"]\n",
    "wealth_order = [\"Low\", \"High\"]\n",
    "\n",
    "df[\"frame\"] = pd.Categorical(df[\"frame\"], categories=frame_order, ordered=True)\n",
    "df[\"wealth_LH\"] = pd.Categorical(df[\"wealth_LH\"], categories=wealth_order, ordered=True)\n",
    "\n",
    "# ----------------------------\n",
    "# Prediction function\n",
    "# ----------------------------\n",
    "def compute_predictions(outcome_var: str, seed: int = 20260228, B: int = 2000) -> pd.DataFrame:\n",
    "    formula = f\"\"\"\n",
    "    {outcome_var}\n",
    "    ~ C(frame) * C(wealth_LH)\n",
    "    + C(context)\n",
    "    + age\n",
    "    + C(gender)\n",
    "    + C(region)\n",
    "    + C(education)\n",
    "    + C(income_category)\n",
    "    + C(employment_status)\n",
    "    + C(occupation_group)\n",
    "    + C(union_membership)\n",
    "    + C(marital_status)\n",
    "    + political_ideology_0_10\n",
    "    \"\"\"\n",
    "\n",
    "    model = smf.ols(formula=formula, data=df).fit(cov_type=\"HC3\")\n",
    "\n",
    "    rng = np.random.default_rng(seed)\n",
    "    beta_hat = model.params.to_numpy()\n",
    "    V = model.cov_params().to_numpy()\n",
    "    beta_draws = rng.multivariate_normal(mean=beta_hat, cov=V, size=B)\n",
    "\n",
    "    design_info = model.model.data.design_info\n",
    "\n",
    "    rows = []\n",
    "    for w in wealth_order:\n",
    "        for f in frame_order:\n",
    "            sub = df[df[\"wealth_LH\"] == w].copy()\n",
    "            cf = sub.copy()\n",
    "            cf[\"frame\"] = pd.Categorical([f]*len(cf), categories=frame_order, ordered=True)\n",
    "\n",
    "            X_cf = patsy.dmatrix(design_info, cf, return_type=\"dataframe\").to_numpy()\n",
    "\n",
    "            mean_point = float((X_cf @ beta_hat).mean())\n",
    "            sim_means = (X_cf @ beta_draws.T).mean(axis=0)\n",
    "            lo, hi = np.quantile(sim_means, [0.025, 0.975])\n",
    "\n",
    "            rows.append({\"Wealth\": w, \"Frame\": f, \"Mean\": mean_point, \"CI 2.5%\": float(lo), \"CI 97.5%\": float(hi)})\n",
    "\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "# Compute predicted values\n",
    "pred_ind = compute_predictions(\"industrial_support_index\")\n",
    "pred_reg = compute_predictions(\"regulation_support_index\")\n",
    "\n",
    "# ----------------------------\n",
    "# Show predicted tables (like Figure 1)\n",
    "# ----------------------------\n",
    "print(\"\\nPredicted values: Industrial Policy Support (marginal standardized, HC3 simulation CI)\")\n",
    "display(pred_ind.round(3).sort_values([\"Wealth\",\"Frame\"]))\n",
    "\n",
    "print(\"\\nPredicted values: Regulation Support (marginal standardized, HC3 simulation CI)\")\n",
    "display(pred_reg.round(3).sort_values([\"Wealth\",\"Frame\"]))\n",
    "\n",
    "# ----------------------------\n",
    "# Plot styling\n",
    "# ----------------------------\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11,\n",
    "    \"axes.labelsize\": 13,    # slightly larger y-label\n",
    "    \"xtick.labelsize\": 11,\n",
    "    \"ytick.labelsize\": 11,\n",
    "    \"legend.fontsize\": 11,\n",
    "})\n",
    "\n",
    "colors = {\"Low\": \"#8B0000\", \"High\": \"#003366\"}   # dark red / dark blue\n",
    "linestyles = {\"Low\": \"-\", \"High\": \"--\"}\n",
    "markers = {\"Low\": \"o\", \"High\": \"s\"}\n",
    "\n",
    "def annotate_two_series(ax, x_positions, y_low, y_high):\n",
    "    \"\"\"\n",
    "    Dynamic placement to avoid overlap:\n",
    "    - At each x, higher point labeled above, lower point labeled below.\n",
    "    \"\"\"\n",
    "    for xi, yl, yh in zip(x_positions, y_low, y_high):\n",
    "        if yl >= yh:\n",
    "            ax.text(xi, yl + 0.22, f\"{yl:.2f}\", color=colors[\"Low\"], fontsize=10,\n",
    "                    ha=\"center\", va=\"bottom\")\n",
    "            ax.text(xi, yh - 0.22, f\"{yh:.2f}\", color=colors[\"High\"], fontsize=10,\n",
    "                    ha=\"center\", va=\"top\")\n",
    "        else:\n",
    "            ax.text(xi, yh + 0.22, f\"{yh:.2f}\", color=colors[\"High\"], fontsize=10,\n",
    "                    ha=\"center\", va=\"bottom\")\n",
    "            ax.text(xi, yl - 0.22, f\"{yl:.2f}\", color=colors[\"Low\"], fontsize=10,\n",
    "                    ha=\"center\", va=\"top\")\n",
    "\n",
    "def plot_panel(ax, pred_df, ylabel):\n",
    "    x = np.arange(len(frame_order))\n",
    "\n",
    "    low = pred_df[pred_df[\"Wealth\"] == \"Low\"].set_index(\"Frame\").loc[frame_order]\n",
    "    high = pred_df[pred_df[\"Wealth\"] == \"High\"].set_index(\"Frame\").loc[frame_order]\n",
    "\n",
    "    y_low = low[\"Mean\"].values\n",
    "    y_high = high[\"Mean\"].values\n",
    "\n",
    "    yerr_low = np.vstack([y_low - low[\"CI 2.5%\"].values, low[\"CI 97.5%\"].values - y_low])\n",
    "    yerr_high = np.vstack([y_high - high[\"CI 2.5%\"].values, high[\"CI 97.5%\"].values - y_high])\n",
    "\n",
    "    ax.errorbar(\n",
    "        x, y_low, yerr=yerr_low,\n",
    "        capsize=4, elinewidth=1.2, linewidth=2.2,\n",
    "        linestyle=linestyles[\"Low\"], marker=markers[\"Low\"],\n",
    "        markersize=6.5, color=colors[\"Low\"], label=\"Wealth: Low\"\n",
    "    )\n",
    "    ax.errorbar(\n",
    "        x, y_high, yerr=yerr_high,\n",
    "        capsize=4, elinewidth=1.2, linewidth=2.2,\n",
    "        linestyle=linestyles[\"High\"], marker=markers[\"High\"],\n",
    "        markersize=6.5, color=colors[\"High\"], label=\"Wealth: High\"\n",
    "    )\n",
    "\n",
    "    annotate_two_series(ax, x, y_low, y_high)\n",
    "\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels([\"Risk frame\", \"Competitiveness frame\"])\n",
    "    ax.set_ylabel(ylabel)\n",
    "\n",
    "    ax.set_ylim(1, 7)\n",
    "    ax.set_yticks(np.arange(1, 8, 1))\n",
    "\n",
    "    # 4-side border\n",
    "    for spine in [\"top\", \"right\", \"left\", \"bottom\"]:\n",
    "        ax.spines[spine].set_visible(True)\n",
    "        ax.spines[spine].set_linewidth(1.0)\n",
    "\n",
    "    ax.grid(False)\n",
    "\n",
    "# ----------------------------\n",
    "# Create figure\n",
    "# ----------------------------\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.6), dpi=300, sharey=True)\n",
    "\n",
    "plot_panel(axes[0], pred_ind, \"Industrial Policy Support (1–7)\")\n",
    "plot_panel(axes[1], pred_reg, \"Regulation Support (1–7)\")\n",
    "\n",
    "# Panel titles with pad\n",
    "axes[0].set_title(\"(a) Industrial Support\", pad=12)\n",
    "axes[1].set_title(\"(b) Regulation Support\", pad=12)\n",
    "\n",
    "# Legend on both panels\n",
    "axes[0].legend(loc=\"upper left\", frameon=False)\n",
    "axes[1].legend(loc=\"upper left\", frameon=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=0.25)\n",
    "\n",
    "fig.savefig(out_png, bbox_inches=\"tight\")\n",
    "fig.savefig(out_pdf, bbox_inches=\"tight\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nSaved:\")\n",
    "print(out_png)\n",
    "print(out_pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53dda8cd-16c1-49df-a799-164236f10a09",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "33614a93-769d-4bbf-9738-7f6c8311857d",
   "metadata": {},
   "source": [
    "# Figure 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "330a1983-72d1-4934-8df1-72261106fe47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicted values: Growth Coalition Alignment Index (marginal standardized, HC3 simulation CI)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Mean</th>\n",
       "      <th>CI 2.5%</th>\n",
       "      <th>CI 97.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>5.579</td>\n",
       "      <td>5.500</td>\n",
       "      <td>5.666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.083</td>\n",
       "      <td>3.988</td>\n",
       "      <td>4.178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>4.822</td>\n",
       "      <td>4.730</td>\n",
       "      <td>4.917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.098</td>\n",
       "      <td>4.003</td>\n",
       "      <td>4.191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Wealth            Frame   Mean  CI 2.5%  CI 97.5%\n",
       "3   High  Competitiveness  5.579    5.500     5.666\n",
       "2   High             Risk  4.083    3.988     4.178\n",
       "1    Low  Competitiveness  4.822    4.730     4.917\n",
       "0    Low             Risk  4.098    4.003     4.191"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2220x1380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved:\n",
      "E:\\불평등 연구\\데이터\\73_AI_성장\\figure_3_growth_alignment.png\n",
      "E:\\불평등 연구\\데이터\\73_AI_성장\\figure_3_growth_alignment.pdf\n"
     ]
    }
   ],
   "source": [
    "# ----------------------------\n",
    "# Paths\n",
    "# ----------------------------\n",
    "base_dir = r\"E:\\\"\n",
    "file_name = \"df_1.xlsx\"\n",
    "data_path = os.path.join(base_dir, file_name)\n",
    "\n",
    "out_png = os.path.join(base_dir, \"figure_3_growth_alignment.png\")\n",
    "out_pdf = os.path.join(base_dir, \"figure_3_growth_alignment.pdf\")\n",
    "\n",
    "# ----------------------------\n",
    "# Load\n",
    "# ----------------------------\n",
    "df = pd.read_excel(data_path)\n",
    "\n",
    "# Pre-registered exclusion\n",
    "if \"attention_check_pass\" in df.columns:\n",
    "    df = df[df[\"attention_check_pass\"] == 1].copy()\n",
    "\n",
    "# Main-text contrast: Low vs High only\n",
    "df = df[df[\"wealth_tertile\"].isin([\"Low\", \"High\"])].copy()\n",
    "df[\"wealth_LH\"] = df[\"wealth_tertile\"]\n",
    "\n",
    "frame_order = [\"Risk\", \"Competitiveness\"]\n",
    "wealth_order = [\"Low\", \"High\"]\n",
    "\n",
    "df[\"frame\"] = pd.Categorical(df[\"frame\"], categories=frame_order, ordered=True)\n",
    "df[\"wealth_LH\"] = pd.Categorical(df[\"wealth_LH\"], categories=wealth_order, ordered=True)\n",
    "\n",
    "# ----------------------------\n",
    "# Prediction function\n",
    "# ----------------------------\n",
    "def compute_predictions(outcome_var: str, seed: int = 20260228, B: int = 2000) -> pd.DataFrame:\n",
    "    formula = f\"\"\"\n",
    "    {outcome_var}\n",
    "    ~ C(frame) * C(wealth_LH)\n",
    "    + C(context)\n",
    "    + age\n",
    "    + C(gender)\n",
    "    + C(region)\n",
    "    + C(education)\n",
    "    + C(income_category)\n",
    "    + C(employment_status)\n",
    "    + C(occupation_group)\n",
    "    + C(union_membership)\n",
    "    + C(marital_status)\n",
    "    + political_ideology_0_10\n",
    "    \"\"\"\n",
    "\n",
    "    model = smf.ols(formula=formula, data=df).fit(cov_type=\"HC3\")\n",
    "\n",
    "    rng = np.random.default_rng(seed)\n",
    "    beta_hat = model.params.to_numpy()\n",
    "    V = model.cov_params().to_numpy()\n",
    "    beta_draws = rng.multivariate_normal(mean=beta_hat, cov=V, size=B)\n",
    "\n",
    "    design_info = model.model.data.design_info\n",
    "\n",
    "    rows = []\n",
    "    for w in wealth_order:\n",
    "        for f in frame_order:\n",
    "            sub = df[df[\"wealth_LH\"] == w].copy()\n",
    "            cf = sub.copy()\n",
    "            cf[\"frame\"] = pd.Categorical([f]*len(cf), categories=frame_order, ordered=True)\n",
    "\n",
    "            X_cf = patsy.dmatrix(design_info, cf, return_type=\"dataframe\").to_numpy()\n",
    "\n",
    "            mean_point = float((X_cf @ beta_hat).mean())\n",
    "            sim_means = (X_cf @ beta_draws.T).mean(axis=0)\n",
    "            lo, hi = np.quantile(sim_means, [0.025, 0.975])\n",
    "\n",
    "            rows.append({\"Wealth\": w, \"Frame\": f, \"Mean\": mean_point, \"CI 2.5%\": float(lo), \"CI 97.5%\": float(hi)})\n",
    "\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "# Compute predicted values\n",
    "pred_align = compute_predictions(\"growth_alignment_index\")\n",
    "\n",
    "# ----------------------------\n",
    "# Show predicted table (like Figure 1/2)\n",
    "# ----------------------------\n",
    "print(\"\\nPredicted values: Growth Coalition Alignment Index (marginal standardized, HC3 simulation CI)\")\n",
    "display(pred_align.round(3).sort_values([\"Wealth\", \"Frame\"]))\n",
    "\n",
    "# ----------------------------\n",
    "# Plot styling (consistent)\n",
    "# ----------------------------\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11,\n",
    "    \"axes.labelsize\": 13,\n",
    "    \"xtick.labelsize\": 11,\n",
    "    \"ytick.labelsize\": 11,\n",
    "    \"legend.fontsize\": 11,\n",
    "})\n",
    "\n",
    "colors = {\"Low\": \"#8B0000\", \"High\": \"#003366\"}   # dark red / dark blue\n",
    "linestyles = {\"Low\": \"-\", \"High\": \"--\"}\n",
    "markers = {\"Low\": \"o\", \"High\": \"s\"}\n",
    "\n",
    "def annotate_two_series(ax, x_positions, y_low, y_high):\n",
    "    for xi, yl, yh in zip(x_positions, y_low, y_high):\n",
    "        if yl >= yh:\n",
    "            ax.text(xi, yl + 0.22, f\"{yl:.2f}\", color=colors[\"Low\"], fontsize=10,\n",
    "                    ha=\"center\", va=\"bottom\")\n",
    "            ax.text(xi, yh - 0.22, f\"{yh:.2f}\", color=colors[\"High\"], fontsize=10,\n",
    "                    ha=\"center\", va=\"top\")\n",
    "        else:\n",
    "            ax.text(xi, yh + 0.22, f\"{yh:.2f}\", color=colors[\"High\"], fontsize=10,\n",
    "                    ha=\"center\", va=\"bottom\")\n",
    "            ax.text(xi, yl - 0.22, f\"{yl:.2f}\", color=colors[\"Low\"], fontsize=10,\n",
    "                    ha=\"center\", va=\"top\")\n",
    "\n",
    "# ----------------------------\n",
    "# Create figure\n",
    "# ----------------------------\n",
    "fig, ax = plt.subplots(figsize=(7.4, 4.6), dpi=300)\n",
    "\n",
    "x = np.arange(len(frame_order))\n",
    "\n",
    "low = pred_align[pred_align[\"Wealth\"] == \"Low\"].set_index(\"Frame\").loc[frame_order]\n",
    "high = pred_align[pred_align[\"Wealth\"] == \"High\"].set_index(\"Frame\").loc[frame_order]\n",
    "\n",
    "y_low = low[\"Mean\"].values\n",
    "y_high = high[\"Mean\"].values\n",
    "\n",
    "yerr_low = np.vstack([y_low - low[\"CI 2.5%\"].values, low[\"CI 97.5%\"].values - y_low])\n",
    "yerr_high = np.vstack([y_high - high[\"CI 2.5%\"].values, high[\"CI 97.5%\"].values - y_high])\n",
    "\n",
    "ax.errorbar(\n",
    "    x, y_low, yerr=yerr_low,\n",
    "    capsize=4, elinewidth=1.2, linewidth=2.2,\n",
    "    linestyle=linestyles[\"Low\"], marker=markers[\"Low\"],\n",
    "    markersize=6.5, color=colors[\"Low\"], label=\"Wealth: Low\"\n",
    ")\n",
    "ax.errorbar(\n",
    "    x, y_high, yerr=yerr_high,\n",
    "    capsize=4, elinewidth=1.2, linewidth=2.2,\n",
    "    linestyle=linestyles[\"High\"], marker=markers[\"High\"],\n",
    "    markersize=6.5, color=colors[\"High\"], label=\"Wealth: High\"\n",
    ")\n",
    "\n",
    "annotate_two_series(ax, x, y_low, y_high)\n",
    "\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels([\"Risk frame\", \"Competitiveness frame\"])\n",
    "ax.set_ylabel(\"Growth Coalition Alignment (1–7)\")\n",
    "\n",
    "ax.set_ylim(1, 7)\n",
    "ax.set_yticks(np.arange(1, 8, 1))\n",
    "\n",
    "# 4-side border\n",
    "for spine in [\"top\", \"right\", \"left\", \"bottom\"]:\n",
    "    ax.spines[spine].set_visible(True)\n",
    "    ax.spines[spine].set_linewidth(1.0)\n",
    "\n",
    "ax.grid(False)\n",
    "\n",
    "# Title with pad (single panel → (a))\n",
    "ax.set_title(\"\", pad=12)\n",
    "\n",
    "ax.legend(loc=\"upper left\", frameon=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(left=0.13)\n",
    "\n",
    "fig.savefig(out_png, bbox_inches=\"tight\")\n",
    "fig.savefig(out_pdf, bbox_inches=\"tight\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nSaved:\")\n",
    "print(out_png)\n",
    "print(out_pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "348b7566-1e92-4a77-8667-64b610d0e9f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "289678e1-9366-4b45-a404-741abce1edb0",
   "metadata": {},
   "source": [
    "# Figure 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "79f37d4f-876c-45f7-8cbd-609883e7eb68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Figure 4 coefficients (HC3 robust, Middle wealth reference):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Panel</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Compet×Low (vs Mid)</th>\n",
       "      <th>CI_low_2.5%</th>\n",
       "      <th>CI_low_97.5%</th>\n",
       "      <th>Compet×High (vs Mid)</th>\n",
       "      <th>CI_high_2.5%</th>\n",
       "      <th>CI_high_97.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(a) Policy Priority</td>\n",
       "      <td>policy_priority</td>\n",
       "      <td>0.0196</td>\n",
       "      <td>-0.1450</td>\n",
       "      <td>0.1842</td>\n",
       "      <td>-0.4054</td>\n",
       "      <td>-0.5716</td>\n",
       "      <td>-0.2393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(b) Industrial Support</td>\n",
       "      <td>industrial_support_index</td>\n",
       "      <td>-0.0179</td>\n",
       "      <td>-0.1904</td>\n",
       "      <td>0.1547</td>\n",
       "      <td>0.4169</td>\n",
       "      <td>0.2438</td>\n",
       "      <td>0.5901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(c) Growth Alignment</td>\n",
       "      <td>growth_alignment_index</td>\n",
       "      <td>-0.2361</td>\n",
       "      <td>-0.4169</td>\n",
       "      <td>-0.0554</td>\n",
       "      <td>0.5282</td>\n",
       "      <td>0.3495</td>\n",
       "      <td>0.7068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Panel                   Outcome  Compet×Low (vs Mid)  \\\n",
       "0     (a) Policy Priority           policy_priority               0.0196   \n",
       "1  (b) Industrial Support  industrial_support_index              -0.0179   \n",
       "2    (c) Growth Alignment    growth_alignment_index              -0.2361   \n",
       "\n",
       "   CI_low_2.5%  CI_low_97.5%  Compet×High (vs Mid)  CI_high_2.5%  \\\n",
       "0      -0.1450        0.1842               -0.4054       -0.5716   \n",
       "1      -0.1904        0.1547                0.4169        0.2438   \n",
       "2      -0.4169       -0.0554                0.5282        0.3495   \n",
       "\n",
       "   CI_high_97.5%  \n",
       "0        -0.2393  \n",
       "1         0.5901  \n",
       "2         0.7068  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3750x1440 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved:\n",
      "E:\\불평등 연구\\데이터\\73_AI_성장\\figure_4_coeffplot_midref.png\n",
      "E:\\불평등 연구\\데이터\\73_AI_성장\\figure_4_coeffplot_midref.pdf\n"
     ]
    }
   ],
   "source": [
    "# ----------------------------\n",
    "# Paths\n",
    "# ----------------------------\n",
    "base_dir = r\"E:\\\"\n",
    "file_name = \"df_1.xlsx\"\n",
    "data_path = os.path.join(base_dir, file_name)\n",
    "\n",
    "out_png = os.path.join(base_dir, \"figure_4_coeffplot_midref.png\")\n",
    "out_pdf = os.path.join(base_dir, \"figure_4_coeffplot_midref.pdf\")\n",
    "\n",
    "# ----------------------------\n",
    "# Load\n",
    "# ----------------------------\n",
    "df = pd.read_excel(data_path)\n",
    "\n",
    "# Pre-registered exclusion\n",
    "if \"attention_check_pass\" in df.columns:\n",
    "    df = df[df[\"attention_check_pass\"] == 1].copy()\n",
    "\n",
    "# Keep ALL wealth tertiles (needed because Middle is the reference)\n",
    "# Ensure ordering so Middle becomes the baseline (reference)\n",
    "wealth_order = [\"Middle\", \"Low\", \"High\"]  # <- Middle first => reference category in patsy\n",
    "frame_order = [\"Risk\", \"Competitiveness\"]\n",
    "\n",
    "df[\"frame\"] = pd.Categorical(df[\"frame\"], categories=frame_order, ordered=True)\n",
    "df[\"wealth_tertile\"] = pd.Categorical(df[\"wealth_tertile\"], categories=wealth_order, ordered=True)\n",
    "\n",
    "# ----------------------------\n",
    "# Model specification\n",
    "# ----------------------------\n",
    "controls = \"\"\"\n",
    "+ C(context)\n",
    "+ age\n",
    "+ C(gender)\n",
    "+ C(region)\n",
    "+ C(education)\n",
    "+ C(income_category)\n",
    "+ C(employment_status)\n",
    "+ C(occupation_group)\n",
    "+ C(union_membership)\n",
    "+ C(marital_status)\n",
    "+ political_ideology_0_10\n",
    "\"\"\"\n",
    "\n",
    "def extract_coefs(outcome_var: str):\n",
    "    \"\"\"\n",
    "    Returns dict with coefficients and 95% CI for:\n",
    "    - Competitiveness × Low (vs Middle)\n",
    "    - Competitiveness × High (vs Middle)\n",
    "    \"\"\"\n",
    "    formula = f\"\"\"\n",
    "    {outcome_var}\n",
    "    ~ C(frame) * C(wealth_tertile)\n",
    "    {controls}\n",
    "    \"\"\"\n",
    "    m = smf.ols(formula=formula, data=df).fit(cov_type=\"HC3\")\n",
    "\n",
    "    term_low = \"C(frame)[T.Competitiveness]:C(wealth_tertile)[T.Low]\"\n",
    "    term_high = \"C(frame)[T.Competitiveness]:C(wealth_tertile)[T.High]\"\n",
    "\n",
    "    # sanity check\n",
    "    missing = [t for t in [term_low, term_high] if t not in m.params.index]\n",
    "    if missing:\n",
    "        raise ValueError(f\"Missing expected term(s): {missing}\\nAvailable terms:\\n{m.params.index.tolist()}\")\n",
    "\n",
    "    def ci(term):\n",
    "        coef = float(m.params[term])\n",
    "        se = float(m.bse[term])\n",
    "        lo = coef - 1.96 * se\n",
    "        hi = coef + 1.96 * se\n",
    "        return coef, lo, hi, se\n",
    "\n",
    "    low_coef, low_lo, low_hi, low_se = ci(term_low)\n",
    "    high_coef, high_lo, high_hi, high_se = ci(term_high)\n",
    "\n",
    "    return {\n",
    "        \"low\":  {\"coef\": low_coef,  \"lo\": low_lo,  \"hi\": low_hi,  \"se\": low_se},\n",
    "        \"high\": {\"coef\": high_coef, \"lo\": high_lo, \"hi\": high_hi, \"se\": high_se},\n",
    "    }\n",
    "\n",
    "panels = [\n",
    "    (\"(a) Policy Priority\", \"policy_priority\"),\n",
    "    (\"(b) Industrial Support\", \"industrial_support_index\"),\n",
    "    (\"(c) Growth Alignment\", \"growth_alignment_index\"),\n",
    "]\n",
    "\n",
    "# Extract coefficients\n",
    "rows = []\n",
    "results = []\n",
    "for title, yvar in panels:\n",
    "    res = extract_coefs(yvar)\n",
    "    results.append((title, yvar, res))\n",
    "    rows.append({\n",
    "        \"Panel\": title,\n",
    "        \"Outcome\": yvar,\n",
    "        \"Compet×Low (vs Mid)\": res[\"low\"][\"coef\"],\n",
    "        \"CI_low_2.5%\": res[\"low\"][\"lo\"],\n",
    "        \"CI_low_97.5%\": res[\"low\"][\"hi\"],\n",
    "        \"Compet×High (vs Mid)\": res[\"high\"][\"coef\"],\n",
    "        \"CI_high_2.5%\": res[\"high\"][\"lo\"],\n",
    "        \"CI_high_97.5%\": res[\"high\"][\"hi\"],\n",
    "    })\n",
    "\n",
    "coef_df = pd.DataFrame(rows)\n",
    "print(\"\\nFigure 4 coefficients (HC3 robust, Middle wealth reference):\")\n",
    "display(coef_df.round(4))\n",
    "\n",
    "# ----------------------------\n",
    "# Plot\n",
    "# ----------------------------\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11,\n",
    "    \"axes.labelsize\": 12,\n",
    "    \"xtick.labelsize\": 11,\n",
    "    \"ytick.labelsize\": 14,\n",
    "})\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(12.5, 4.8), dpi=300, sharey=True)\n",
    "\n",
    "marker_color = \"#8B0000\"  # red marker\n",
    "ci_color = \"black\"      # dark red CI\n",
    "\n",
    "# y-positions for two points within each panel\n",
    "y_low_pos = 0.18\n",
    "y_high_pos = -0.18\n",
    "\n",
    "for ax, (title, yvar, res) in zip(axes, results):\n",
    "\n",
    "    # reference line at 0\n",
    "    ax.axvline(0, linewidth=1.2, linestyle=\"--\", color=\"red\")\n",
    "\n",
    "    # LOW vs MID\n",
    "    c = res[\"low\"][\"coef\"]; lo = res[\"low\"][\"lo\"]; hi = res[\"low\"][\"hi\"]\n",
    "    ax.errorbar(\n",
    "        x=c, y=y_low_pos,\n",
    "        xerr=[[c - lo], [hi - c]],\n",
    "        fmt=\"o\", color=marker_color, ecolor=ci_color,\n",
    "        elinewidth=2.0, capsize=4, markersize=7\n",
    "    )\n",
    "    ax.text(c, y_low_pos + 0.06, f\"{c:.2f}\", color=marker_color, fontsize=10,\n",
    "            ha=\"center\", va=\"bottom\")\n",
    "\n",
    "    # HIGH vs MID\n",
    "    c = res[\"high\"][\"coef\"]; lo = res[\"high\"][\"lo\"]; hi = res[\"high\"][\"hi\"]\n",
    "    ax.errorbar(\n",
    "        x=c, y=y_high_pos,\n",
    "        xerr=[[c - lo], [hi - c]],\n",
    "        fmt=\"o\", color=marker_color, ecolor=ci_color,\n",
    "        elinewidth=2.0, capsize=4, markersize=7\n",
    "    )\n",
    "    ax.text(c, y_high_pos - 0.06, f\"{c:.2f}\", color=marker_color, fontsize=10,\n",
    "            ha=\"center\", va=\"top\")\n",
    "\n",
    "    # Panel formatting\n",
    "    ax.set_title(title, pad=12)\n",
    "    ax.set_xlabel(\"Competitiveness × Wealth (vs Middle)\")\n",
    "\n",
    "    ax.set_yticks([y_low_pos, y_high_pos])\n",
    "    ax.set_yticklabels([\"Low\", \"High\"])  # indicates which point is which\n",
    "\n",
    "    ax.set_ylim(-0.6, 0.6)\n",
    "\n",
    "    # 4-side border\n",
    "    for spine in [\"top\", \"right\", \"left\", \"bottom\"]:\n",
    "        ax.spines[spine].set_visible(True)\n",
    "        ax.spines[spine].set_linewidth(1.0)\n",
    "\n",
    "    ax.grid(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=0.25)\n",
    "\n",
    "fig.savefig(out_png, bbox_inches=\"tight\")\n",
    "fig.savefig(out_pdf, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nSaved:\")\n",
    "print(out_png)\n",
    "print(out_pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca1314ce-ffb2-4af8-b5bc-456ed341ade2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "55821a8a-762d-4fab-b19d-64b32ae2832a",
   "metadata": {},
   "source": [
    "# Table A2 Randomization Balance Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "54cc5a35-a87a-413f-9744-60f59170819e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Appendix Table A3. Randomization balance across 2×2 cells\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable</th>\n",
       "      <th>Risk × RegulationContext</th>\n",
       "      <th>Risk × IndustrialSupportContext</th>\n",
       "      <th>Competitiveness × RegulationContext</th>\n",
       "      <th>Competitiveness × IndustrialSupportContext</th>\n",
       "      <th>p (joint test)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age (mean)</td>\n",
       "      <td>44.851</td>\n",
       "      <td>44.004</td>\n",
       "      <td>44.890</td>\n",
       "      <td>44.512</td>\n",
       "      <td>0.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female (prop.)</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.490</td>\n",
       "      <td>0.496</td>\n",
       "      <td>0.512</td>\n",
       "      <td>0.386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Capital region (prop.)</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.497</td>\n",
       "      <td>0.473</td>\n",
       "      <td>0.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Married (prop.)</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.484</td>\n",
       "      <td>0.622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ideology 0–10 (mean)</td>\n",
       "      <td>5.468</td>\n",
       "      <td>5.301</td>\n",
       "      <td>5.251</td>\n",
       "      <td>5.225</td>\n",
       "      <td>0.101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Income (million KRW, mean)</td>\n",
       "      <td>5.193</td>\n",
       "      <td>5.159</td>\n",
       "      <td>5.312</td>\n",
       "      <td>5.127</td>\n",
       "      <td>0.193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Income refusal (prop.)</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bachelor (prop.)</td>\n",
       "      <td>0.441</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.430</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Graduate degree (prop.)</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>High school or less (prop.)</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Permanent employee (prop.)</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.457</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.451</td>\n",
       "      <td>0.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Non-regular employee (prop.)</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Self-employed (prop.)</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.181</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Unemployed (prop.)</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Not in labor force (prop.)</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.097</td>\n",
       "      <td>0.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Union member (prop.)</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.159</td>\n",
       "      <td>0.282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Variable Risk × RegulationContext  \\\n",
       "0                     Age (mean)                   44.851   \n",
       "1                 Female (prop.)                    0.466   \n",
       "2         Capital region (prop.)                    0.493   \n",
       "3                Married (prop.)                    0.493   \n",
       "4           Ideology 0–10 (mean)                    5.468   \n",
       "5     Income (million KRW, mean)                    5.193   \n",
       "6         Income refusal (prop.)                    0.022   \n",
       "7               Bachelor (prop.)                    0.441   \n",
       "8        Graduate degree (prop.)                    0.134   \n",
       "9    High school or less (prop.)                    0.190   \n",
       "10    Permanent employee (prop.)                    0.436   \n",
       "11  Non-regular employee (prop.)                    0.222   \n",
       "12         Self-employed (prop.)                    0.148   \n",
       "13            Unemployed (prop.)                    0.057   \n",
       "14    Not in labor force (prop.)                    0.115   \n",
       "15          Union member (prop.)                    0.149   \n",
       "\n",
       "   Risk × IndustrialSupportContext Competitiveness × RegulationContext  \\\n",
       "0                           44.004                              44.890   \n",
       "1                            0.490                               0.496   \n",
       "2                            0.473                               0.497   \n",
       "3                            0.517                               0.494   \n",
       "4                            5.301                               5.251   \n",
       "5                            5.159                               5.312   \n",
       "6                            0.023                               0.026   \n",
       "7                            0.457                               0.436   \n",
       "8                            0.124                               0.113   \n",
       "9                            0.155                               0.164   \n",
       "10                           0.457                               0.482   \n",
       "11                           0.211                               0.185   \n",
       "12                           0.115                               0.154   \n",
       "13                           0.079                               0.062   \n",
       "14                           0.118                               0.106   \n",
       "15                           0.154                               0.183   \n",
       "\n",
       "   Competitiveness × IndustrialSupportContext p (joint test)  \n",
       "0                                      44.512          0.538  \n",
       "1                                       0.512          0.386  \n",
       "2                                       0.473          0.682  \n",
       "3                                       0.484          0.622  \n",
       "4                                       5.225          0.101  \n",
       "5                                       5.127          0.193  \n",
       "6                                       0.028          0.904  \n",
       "7                                       0.430          0.750  \n",
       "8                                       0.155          0.103  \n",
       "9                                       0.172          0.325  \n",
       "10                                      0.451          0.339  \n",
       "11                                      0.199          0.326  \n",
       "12                                      0.181          0.004  \n",
       "13                                      0.061          0.299  \n",
       "14                                      0.097          0.548  \n",
       "15                                      0.159          0.282  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Appendix Table A4. Manipulation checks (means by 2×2 cells)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Measure</th>\n",
       "      <th>Risk × RegulationContext</th>\n",
       "      <th>Risk × IndustrialSupportContext</th>\n",
       "      <th>Competitiveness × RegulationContext</th>\n",
       "      <th>Competitiveness × IndustrialSupportContext</th>\n",
       "      <th>p (frame effect)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Manipulation check: Growth emphasis</td>\n",
       "      <td>4.010</td>\n",
       "      <td>4.023</td>\n",
       "      <td>5.367</td>\n",
       "      <td>5.397</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Manipulation check: Risk emphasis</td>\n",
       "      <td>5.376</td>\n",
       "      <td>5.397</td>\n",
       "      <td>4.023</td>\n",
       "      <td>4.004</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Measure Risk × RegulationContext  \\\n",
       "0  Manipulation check: Growth emphasis                    4.010   \n",
       "1    Manipulation check: Risk emphasis                    5.376   \n",
       "\n",
       "  Risk × IndustrialSupportContext Competitiveness × RegulationContext  \\\n",
       "0                           4.023                               5.367   \n",
       "1                           5.397                               4.023   \n",
       "\n",
       "  Competitiveness × IndustrialSupportContext p (frame effect)  \n",
       "0                                      5.397                   \n",
       "1                                      4.004                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved files:\n",
      "E:\\불평등 연구\\데이터\\73_AI_성장\\appendix_table_A3_balance.xlsx\n",
      "E:\\불평등 연구\\데이터\\73_AI_성장\\appendix_table_A4_manipchecks.xlsx\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "from IPython.display import display\n",
    "\n",
    "# ----------------------------\n",
    "# 0) Paths\n",
    "# ----------------------------\n",
    "base_dir = r\"E:\\불평등 연구\\데이터\\73_AI_성장\"\n",
    "data_path = os.path.join(base_dir, \"df_1.xlsx\")\n",
    "\n",
    "out_A3 = os.path.join(base_dir, \"appendix_table_A3_balance.xlsx\")\n",
    "out_A4 = os.path.join(base_dir, \"appendix_table_A4_manipchecks.xlsx\")\n",
    "\n",
    "# ----------------------------\n",
    "# 1) Load data\n",
    "# ----------------------------\n",
    "df = pd.read_excel(data_path)\n",
    "\n",
    "# OPTIONAL: If you want balance on analytic sample only, uncomment:\n",
    "# df = df[df[\"attention_check_pass\"] == 1].copy()\n",
    "\n",
    "# Ensure consistent condition coding\n",
    "# frame: Risk / Competitiveness\n",
    "# context: RegulationContext / IndustrialSupportContext (or your exact labels)\n",
    "df[\"frame\"] = df[\"frame\"].astype(str)\n",
    "df[\"context\"] = df[\"context\"].astype(str)\n",
    "\n",
    "# Create 4-cell condition label\n",
    "df[\"cell\"] = df[\"frame\"] + \" × \" + df[\"context\"]\n",
    "\n",
    "cell_order = [\n",
    "    \"Risk × RegulationContext\",\n",
    "    \"Risk × IndustrialSupportContext\",\n",
    "    \"Competitiveness × RegulationContext\",\n",
    "    \"Competitiveness × IndustrialSupportContext\",\n",
    "]\n",
    "# Keep only cells that exist in data (in case of naming differences)\n",
    "existing = [c for c in cell_order if c in set(df[\"cell\"])]\n",
    "if len(existing) == 4:\n",
    "    df[\"cell\"] = pd.Categorical(df[\"cell\"], categories=cell_order, ordered=True)\n",
    "else:\n",
    "    # fallback: sort alphabetically if labels differ\n",
    "    df[\"cell\"] = pd.Categorical(df[\"cell\"], categories=sorted(df[\"cell\"].unique()), ordered=True)\n",
    "    cell_order = list(df[\"cell\"].cat.categories)\n",
    "\n",
    "# ----------------------------\n",
    "# Helper: group mean/proportion\n",
    "# ----------------------------\n",
    "def cell_stats(series):\n",
    "    \"\"\"Return mean (for numeric) or proportion==1 (for binary) by 4 cells.\"\"\"\n",
    "    out = {}\n",
    "    for c in cell_order:\n",
    "        s = series[df[\"cell\"] == c]\n",
    "        out[c] = float(np.nanmean(s))\n",
    "    return out\n",
    "\n",
    "def joint_balance_pvalue(varname):\n",
    "    \"\"\"\n",
    "    Joint test that all treatment terms (frame, context, interaction) are zero in:\n",
    "    var ~ C(frame) * C(context)\n",
    "    Uses OLS; fine for balance diagnostics (including binary indicators).\n",
    "    \"\"\"\n",
    "    m = smf.ols(f\"{varname} ~ C(frame) * C(context)\", data=df).fit()\n",
    "    # all non-intercept coefficients jointly = 0\n",
    "    terms = [t for t in m.params.index if t != \"Intercept\"]\n",
    "    if not terms:\n",
    "        return np.nan\n",
    "    R = np.eye(len(m.params))[1:]  # exclude intercept row\n",
    "    p = float(m.f_test(R).pvalue)\n",
    "    return p\n",
    "\n",
    "# ----------------------------\n",
    "# 2) Build Appendix Table A3 (Balance)\n",
    "#    Choose a compact set of covariates for balance display.\n",
    "#    (You can add/remove variables easily.)\n",
    "# ----------------------------\n",
    "\n",
    "# Create standardized/binary indicators for categorical variables to report proportions.\n",
    "df[\"female\"] = (df[\"gender\"].astype(str) == \"Female\").astype(int)\n",
    "df[\"capital_region\"] = (df[\"region\"].astype(str) == \"Capital region\").astype(int)\n",
    "df[\"married\"] = (df[\"marital_status\"].astype(str) == \"Married\").astype(int)\n",
    "df[\"bachelor\"] = (df[\"education\"].astype(str) == \"Bachelor’s degree\").astype(int)\n",
    "df[\"grad\"] = (df[\"education\"].astype(str) == \"Graduate degree\").astype(int)\n",
    "df[\"hs_or_less\"] = (df[\"education\"].astype(str) == \"High school or less\").astype(int)\n",
    "\n",
    "df[\"permanent\"] = (df[\"employment_status\"].astype(str) == \"Permanent employee\").astype(int)\n",
    "df[\"nonregular\"] = (df[\"employment_status\"].astype(str) == \"Non-regular employee\").astype(int)\n",
    "df[\"selfemp\"] = (df[\"employment_status\"].astype(str) == \"Self-employed\").astype(int)\n",
    "df[\"unemployed\"] = (df[\"employment_status\"].astype(str).str.contains(\"Unemployed\", na=False)).astype(int)\n",
    "df[\"nlf\"] = (df[\"employment_status\"].astype(str).str.contains(\"Not in labor force\", na=False)).astype(int)\n",
    "\n",
    "df[\"union_yes\"] = (df[\"union_membership\"].astype(str) == \"Yes\").astype(int)\n",
    "\n",
    "# income: continuous (if you have it) and/or refusal indicator\n",
    "if \"income_monthly_million_krw\" in df.columns:\n",
    "    df[\"income_cont\"] = pd.to_numeric(df[\"income_monthly_million_krw\"], errors=\"coerce\")\n",
    "else:\n",
    "    df[\"income_cont\"] = np.nan\n",
    "\n",
    "df[\"income_refuse\"] = (df[\"income_category\"].astype(str).str.contains(\"Prefer not\", na=False)).astype(int)\n",
    "\n",
    "balance_vars = [\n",
    "    (\"Age (mean)\", \"age\"),\n",
    "    (\"Female (prop.)\", \"female\"),\n",
    "    (\"Capital region (prop.)\", \"capital_region\"),\n",
    "    (\"Married (prop.)\", \"married\"),\n",
    "    (\"Ideology 0–10 (mean)\", \"political_ideology_0_10\"),\n",
    "    (\"Income (million KRW, mean)\", \"income_cont\"),\n",
    "    (\"Income refusal (prop.)\", \"income_refuse\"),\n",
    "    (\"Bachelor (prop.)\", \"bachelor\"),\n",
    "    (\"Graduate degree (prop.)\", \"grad\"),\n",
    "    (\"High school or less (prop.)\", \"hs_or_less\"),\n",
    "    (\"Permanent employee (prop.)\", \"permanent\"),\n",
    "    (\"Non-regular employee (prop.)\", \"nonregular\"),\n",
    "    (\"Self-employed (prop.)\", \"selfemp\"),\n",
    "    (\"Unemployed (prop.)\", \"unemployed\"),\n",
    "    (\"Not in labor force (prop.)\", \"nlf\"),\n",
    "    (\"Union member (prop.)\", \"union_yes\"),\n",
    "]\n",
    "\n",
    "rows_A3 = []\n",
    "for label, var in balance_vars:\n",
    "    stats = cell_stats(df[var])\n",
    "    p = joint_balance_pvalue(var)\n",
    "    row = {\"Variable\": label}\n",
    "    for c in cell_order:\n",
    "        row[c] = stats[c]\n",
    "    row[\"p (joint test)\"] = p\n",
    "    rows_A3.append(row)\n",
    "\n",
    "A3 = pd.DataFrame(rows_A3)\n",
    "\n",
    "# Formatting\n",
    "A3_disp = A3.copy()\n",
    "for c in cell_order:\n",
    "    A3_disp[c] = A3_disp[c].map(lambda x: \"\" if pd.isna(x) else f\"{x:.3f}\")\n",
    "A3_disp[\"p (joint test)\"] = A3_disp[\"p (joint test)\"].map(lambda x: \"\" if pd.isna(x) else f\"{x:.3f}\")\n",
    "\n",
    "print(\"\\nAppendix Table A3. Randomization balance across 2×2 cells\")\n",
    "display(A3_disp)\n",
    "\n",
    "A3.to_excel(out_A3, index=False)\n",
    "\n",
    "# ----------------------------\n",
    "# 3) Build Appendix Table A4 (Manipulation checks)\n",
    "# ----------------------------\n",
    "# Expected columns: manip_check_growth, manip_check_risk\n",
    "# If your df uses different names, change here.\n",
    "mc_growth = \"manip_check_growth\"\n",
    "mc_risk = \"manip_check_risk\"\n",
    "\n",
    "if mc_growth not in df.columns or mc_risk not in df.columns:\n",
    "    raise ValueError(\"Manipulation check columns not found. Expected: manip_check_growth, manip_check_risk\")\n",
    "\n",
    "def mean_ci(series):\n",
    "    s = series.dropna().astype(float)\n",
    "    n = len(s)\n",
    "    m = float(s.mean())\n",
    "    se = float(s.std(ddof=1) / np.sqrt(n)) if n > 1 else np.nan\n",
    "    lo = m - 1.96*se if n > 1 else np.nan\n",
    "    hi = m + 1.96*se if n > 1 else np.nan\n",
    "    return m, lo, hi, n\n",
    "\n",
    "rows_A4 = []\n",
    "for var, label in [(mc_growth, \"Manipulation check: Growth emphasis\"),\n",
    "                   (mc_risk, \"Manipulation check: Risk emphasis\")]:\n",
    "\n",
    "    row = {\"Measure\": label}\n",
    "    for c in cell_order:\n",
    "        m, lo, hi, n = mean_ci(df.loc[df[\"cell\"] == c, var])\n",
    "        row[c] = m\n",
    "        row[c + \" (CI)\"] = (lo, hi)\n",
    "        row[c + \" (N)\"] = n\n",
    "\n",
    "    # Primary test for manipulation:\n",
    "    # Growth check should differ by frame (Competitiveness vs Risk), not necessarily by context.\n",
    "    # Use OLS: var ~ C(frame) + C(context) + C(frame):C(context)\n",
    "    mfit = smf.ols(f\"{var} ~ C(frame) * C(context)\", data=df).fit(cov_type=\"HC3\")\n",
    "\n",
    "    # Extract p-value for frame effect (Competitiveness vs Risk)\n",
    "    # term name:\n",
    "    term = \"C(frame)[T.Competitiveness]\"\n",
    "    p_frame = float(mfit.pvalues.get(term, np.nan))\n",
    "\n",
    "    row[\"p (frame effect)\"] = p_frame\n",
    "    rows_A4.append(row)\n",
    "\n",
    "A4 = pd.DataFrame(rows_A4)\n",
    "\n",
    "# Pretty display version\n",
    "A4_disp = A4[[\"Measure\"] + cell_order + [\"p (frame effect)\"]].copy()\n",
    "for c in cell_order:\n",
    "    A4_disp[c] = A4_disp[c].map(lambda x: \"\" if pd.isna(x) else f\"{x:.3f}\")\n",
    "A4_disp[\"p (frame effect)\"] = A4_disp[\"p (frame effect)\"].map(lambda x: \"\" if pd.isna(x) else f\"{x:.3f}\")\n",
    "\n",
    "print(\"\\nAppendix Table A4. Manipulation checks (means by 2×2 cells)\")\n",
    "display(A4_disp)\n",
    "\n",
    "A4.to_excel(out_A4, index=False)\n",
    "\n",
    "print(\"\\nSaved files:\")\n",
    "print(out_A3)\n",
    "print(out_A4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d41e3da-ff68-47ee-a90c-b6dfb3a688a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0b5a58d7-8bbf-4afa-92cf-5b9f38fdbacc",
   "metadata": {},
   "source": [
    "## Full wealth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "de9f99b7-7047-47e4-b719-42793d32d20e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Appendix Figure C1 predicted values (Policy Priority, full tertiles)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Mean</th>\n",
       "      <th>CI 2.5%</th>\n",
       "      <th>CI 97.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>High</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>3.191</td>\n",
       "      <td>3.104</td>\n",
       "      <td>3.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.400</td>\n",
       "      <td>4.318</td>\n",
       "      <td>4.483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>3.741</td>\n",
       "      <td>3.654</td>\n",
       "      <td>3.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.525</td>\n",
       "      <td>4.438</td>\n",
       "      <td>4.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Middle</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>3.669</td>\n",
       "      <td>3.588</td>\n",
       "      <td>3.748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Middle</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.472</td>\n",
       "      <td>4.390</td>\n",
       "      <td>4.553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Wealth            Frame   Mean  CI 2.5%  CI 97.5%\n",
       "5    High  Competitiveness  3.191    3.104     3.275\n",
       "4    High             Risk  4.400    4.318     4.483\n",
       "1     Low  Competitiveness  3.741    3.654     3.824\n",
       "0     Low             Risk  4.525    4.438     4.611\n",
       "3  Middle  Competitiveness  3.669    3.588     3.748\n",
       "2  Middle             Risk  4.472    4.390     4.553"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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yAAAAAChvEbxoHeiIXtzRO3t+i7msmMcrFPN/EWiMoHz0XI/52Qi6x/zliBEjsjnUvFgf86nRG7u9F6DdcsstLcHQmLc7/PDD02677Zb1DY8+5TGHedttt6Xf/OY32Vxy3h//+McsUBtzqDG/FoH2ENvGHO56662XzW3GNvfff38WrC0MgL755pvpjDPOyJb2Ouyww1oCXfm5x5jLjrnH/IV5Eei56KKL0sSJE1u2i3ntPffcMwtGtmUuMJ7/mJMuFI8p5k633nrrbL62b9++2TxtzKX+/e9/T5dccknLfGXMlx599NHZ/TbddNNZ9h+JKssss0z2+z//+c+i+fDotd2WOeiOirnq/OsZF+sVPk8RG4j+8XMSc9SdEXPl8djjNcq79tpr2524E69j9EPPi3n2eC/OSbweEexunTQ0YMCAdOihh2bzyiuttFL2nornI94vf/nLX9I111yT9VwPMZcdn9cIuMZnpD1+//vfZ5+3/Gct3sfxvo3zThwzYi9xHojkgsL3QiQ5RBJL63PEnPzyl79Mp512WtG6+CwfcMABabvttktrrbVW6tevXxacj89KJG5ceOGF2fHz4nMZc/Rzio+EP/zhD9m8euvnMsYaz2U+lhEi+SZeq3hccT6IYxaeT2Ynzjmtz9Grrrpq9rzF5yPiLNGPPuIV8Xq99dZb2f7vueeedPfdd88SN+gs8aQ5E08qkRJUO6CEbQuOOOKIWbY/44wz2rx9e9oWRGmQwvsutthiuaeeeqrNx3ruuefmWVqmPW0LRo0aNUs/lCWWWGK+9BTpaHuFOZVqyS/f/OY329TjqvV76Pbbb29X2ZWbb745KytUeOxoR9GZsR9zzDG5qVOnznHb6BvUepso+ZTvqRPlwJqamua4/aOPPjpLT5norTQvURZtjTXWKNou3jd33333PLcdMWJE1heucNt4HAAAAADUjtn1Sj/ggAPm+zguuuiiWcYRZatjfHMyYcKE3B577DHLdvvss0+7544Ly71HOec5iXLa0b62cJvoBb3pppu2lFe/884759p7PfqcF27ft2/frL93R+cu119//axU9pxEKe511113lu3aUpY95lVbb3fooYfOs2x1jGeDDTaYpU1D6zYP82ph0LrMeVu1tW3BvMrrd0R74hLR0qHwvgsuuGBu3Lhx7Tpe65bH0b98bk477bSi+0frgGh7MLf57/DSSy/lVlpppVnee/Myp9YQa6+9dm706NFz3C7GM7u21k8++eQ8jxmvXZSEL9xu5513nuscfYj4xfDhw4u2i8/0Bx98MMdtWvel33bbbXOfffZZri2iRH20iPnjH/8423+PVirxuWn9Xo7t2iI+bxH7uuOOO3LdRTxJPKnUJA/UUPLAb3/721m2jeB5c3Nzt/yRji/Ehff91a9+lSvVSTS+XMYfpNZ9hF544YUuH1NnxtmeE2bso61/0LrCbbfdNku/mo6O/aCDDmrTMefUc+irX/3qPL8Uz663XL9+/eb5R+7MM88s2qaxsXGu/6Ga13/K4ovIvL4kAgAAAFA94mKl2QUx5qcInDc0NBSNYbnllptrwC4v5rIieN/6McTFXu1NHoikgDFjxszzmNdee+1s5wHr6+vbFHB+7bXXZgnCRBC5I3OXK6ywQpvmzCMQNWjQoFke79zmAiMAGnOUhdv86Ec/yrVVBP5WXnnlou0jADU3tZY8EM9/6/d+zNm21cSJE3MLL7xw0fY33njjHO//zjvvzPLeu+CCC9p8vAj2L7XUUkXbR7C2vckDQ4YMaVNgOO7TOllndr3uWwfcV1tttaJt9txzzzYHlOM1aZ3g89Of/nS2950+fXr2uS9MxIhkna4SiUytL3qNBKRyIp4knlRq9aWqeED3i1Ihr776alb+KHptFJY9yYtSR0sttVS3HD/KbxSK3ialEI8/yhJFa4S8tddeOysJEq0TKlG0mbjiiiu6pd3EnMRz+LWvfa3ldpRPam9foxBliy644II23bewv1mheOxRDqct2xeWeBk3blxLiZvZiXJK559/ftG6KKsUJaXaKsqFRRmawtJu+ZJRAAAAAFS/mINqLcolz0833HDDLP2TY94rSj3PS5SWjjLQreffWs+btUWUgm5sbJzn/fbee++WMuSF8mXK5yXKWbe+X2FJ6PaIx9mWOfMo/33eeefNMideWDK/tXheC98fUeY9ymq3VbyPWr8ObZ1rrRXx/v3Od75TtC5aF7RVlDD//PPPW25H+4hoATC393i+tUaINh/HHHNMm483ePDgdPrpp3f6NY33VlvOM3GfKAXful/73Nx6661FrUGiJcaVV16Z6uvr2/yaRJn2wrn66EEf8/GtxXkrys7nLb300lnZ/u6KW0VZ+oUXXjhVM/Ek8aT2kjxQBeLNPLslerJET5voqdP65B99rCKoHl/Kukv07yjUkRNDZ51yyinZ4y/8473NNttk/VXa8kW5XMUXkPacgLpKJKF09gv4IYcckr0322J2/bo22GCDWcYxJ9EHqXWfrP/+979zvH98sS/88hB9jqJXWXtFP51C0QcJAAAAgNpQ2De5VMkDN910U9HtmOfaY4892rx99INu3WM7+kYXBvXmJXo877XXXm26b8xXx7xfaz/4wQ/afLzNNtuszfOAcxK9vHfeeec233+33XbLEhcKjRw5co73jwBqof/5n/+Za5/x2dlhhx2ygHNeBHXz/cz5/x100EFFt5944omsL3lbXHPNNUW399lnn+z9OTsRd4g4S6Ff/OIX7R5vBPOj/3reAw88kPVzb6uhQ4emLbbYos33jxhJez4rrd+3xx57bJvn+PPiQs5hw4a13B4/fnx66qmn5hlXivtNmTIlVVPcan4TTxJPaq/5l2ZCWYjsmviSGEH17j5ZxBetf/zjHy23f/Ob32TZRt1V6aBQ/GGNLKHWGYXxpSEy2iLTrZLtuuuuXbq/+HIZJ8HRo0eniRMnZlUaChMuCrPDCsUXrvaOZdttt23zfeNkHe/Zwv9wtf5iMy+rrLJKevPNN1tut864LnTvvfcW3W7Pf6jm9kfqoYce6tB+AAAAAKgOhVczdrdoV/zoo48Wrdtvv/3avZ/9998/u7o4L+YNX3jhhexq+baIYGZ7rnSNebx77rmn5fZyyy2XXRXcnu0LzW0ecE46crFdBJcLrxyfU4AsgpSFV2/Hc/PNb34zdXT+8Z133imaf+zOCwUrTbxH11lnnTRq1KiipIDf/va3c93urbfemmUut3UiQqEIfhdWPI73bFsDlYUicSAqN+ePHZUPnnnmmaIrkudm++23b3fsplAcL+bgZ3d1dsQJWj8nnZk3v//++1tux3432WSTWa40j2olTU1N2e1IHIiEjLPPPjt1hYiLxWcvH/+ImMhFF12Uvve976VqJZ7UduJJ/z/JAzUmSinFH5L5kWW0++67F5Vcii9GkV0W2aKRtRrZq90hTlbxIW2dnXPyySen0047LVWD9dZbr9P7ePfdd7OMwT/96U/ZSb4jPv3003Zvs+aaa7br/lGurPBk35HtC02YMGGO942KFK0zNjsisqoLFf6xAQAAAKC6zS4AN7c5qa4WZZZbz9ttvPHG7d5PlJyOq+JnzJhRdAV3W5MHOjuPt8Yaa3Rq+448520N1s5tm48++igrPR2BrLnNPUaQqqPl0s0/tu1q/sLkgeuvvz6deeaZc630EBcjRvJNXsQzZlcRY06vabRL7qjZvaZtfT+297hLLLHELOvi8zK7c1c8h9EiO69Pnz4dji+19X0bsaVouZ13zjnnZEk50Q4irqJfZJFFUkfFeWLrrbdO//rXv1rWff/7389uxxXg8W9zqjRRqcST2rd9oVqNJ0keqAInnHDCLOsiGyvKZcQJtfCDHB/w6PcTWUHzyrLrrPjD9u1vfzvrr5UX2WI///nPsyW+HEWZmq9//etZdlnrzNCOiCygyNIpLCUSWWRxUovyJtUiMhg7I7L0Tj311KLeTR0RJYPaKzIH26OwXFNXbD+7PkqFmaWF2lpWrS2VMCL7rr2lnAAAAACoPLObv5qfyQMRvG6tvQGUEIHEIUOGpNdee22u+67EecA5aU+lg8IAc2sffvjhLMkDreceX3rppS6rSDF27Ngu2U81iQsYf/zjH6epU6e2vCZ33XXXHK/Sj6SBP/zhD22uOjC71/T2228vyWva3s/K7JIE5tQaoPVjnDx5cqqvr+/Wx3jSSSdlbVI++eSToquxY4nAfsSevvGNb2RJUfFzdskQc/PrX/863XfffUWP+bbbbsuWSI6ImFV+//F7rKtk4kkd3/7LGo0nSR6oAtEOYG6i1NNRRx2VZZzmRUWAZZZZJh133HHdOrbo9xN/nG+55ZZZ/i2+dMZyxRVXZLf79++f/eGOMk9bbbVVh/7Ixheu1iJ5oaPlQspVZ3qkRXbehRde2CXjyH/x6kxPofm9/ZzEH76u7J00uz+MkgcAAAAAqt/AgQNnWRdXos8vswvQ9O3bt0P7ah14ac+Vo+U6Dzg3HXmeZhe4nN3z1J0B/o4E5ardkksumXbaaaei2ES0LphT8kAEkwsvxIyLEuNCzLkpl9e0Oz8rpXiMkXgTlQCixH3rc2cEcx944IFsCZHIEFd877bbblnCSOue9bMT949+9XHxa+sxRHJEJJnEEqIFdlRhiRhTxK4irlZpxJO63udVHk/qmvQgyloE4h955JG02mqrFa3/6U9/mvXN6U6RwfaXv/wl+wM9t/I++cy/SDaIHiRRZufOO+9s9/HiRN7a//7v/87XzN75oT29wgpFSZnWJ/r447rDDjtkz1P0aIkkkzgxRWmXyLYsXE455ZRUrbr7C3ZheTcAAAAAqle0a219ZW/0Rp9fCnuw5+cSOxpAaX3Fbet9V5uOtBGY3VXJhWXe58f8o7nHObcuKHTrrbfOMVYQLQsKRZLBvALFtfCaluoxRqn9F154IatiHe2452TmzJlZnOsXv/hFWnnllbOEj7ZUSNluu+3Syy+/nLUsaF2qvvVV4A8//HD60Y9+lJZffvksmB6tsyuJeFLXG1/l8SSVB2rEUkstlf76179mGVX5MhvTp09Phx9+eHryySe7rJTOnETWVyyvvPJKuuOOO9L999+fnXALy84Uev7557MT0C9/+cusRE1b5VsgRO+ivEiciASKf//73+0uUVJN4o9o6xYX0Zso3hdt7R/W2bI05Wx2pZoi83Dw4MElz+4DAAAAoHJET/W4OCpayubFvGhc0To/yl+3vlox5oHjis+OJBDEmOe272oT85/tfYytn6Mwu57srecf4+rq/fbbL5VLT/NqlE8A+Pjjj7PbcaVwVCo+4ogjZkn2iDL57WlZMLvXNFpYRLWDrhBl88tB68cYn4+jjz66S/Y9ryoBMaceMaIIQv/nP//JqmxHxYFIFpjdVewRA/njH/+YxYJimVcP+nhv/P73v88qdcf94xix/4hPzS54G7G1CKZHe4qoVDG7KjPVQjyptuNJkgdqSFQeiED8ySef3LLu6aefTtddd1064IAD5tsYYsm3S4gvzdGnJkrQxFL4RSsykyKrbK211ko777xzm49xxhlnZF/OfvaznxU9zs033zwrNbP00kunWhT/WSks8RPZZlGaZ3Y9ueakPWXJKk2UF4vnJP4zlbfnnnum3XffvaTjAgAAAKDybLvttkXJAxGI+uc//9llPZE7Uka/I/Oi48aN65L2B5Uinqf2Jg+0tU1EXODX+j7zaklM58R8byRo/O53vytqXdA6eSASBwpjE9HyoC0xidavaQTDq+01bf0Yw/x+jPE6xjk1lnwSyBNPPJFdpHrbbbdlvxdqbm7OXr9XX321TdVEIhC8yy67ZEu+wsqjjz6aXVn/j3/8I7344ouz9LqPuEEct7svzC0V8aTajidpW1BjfvzjH6dll122aN3pp59e9AafnyKR4LDDDsv+OEfbgtNOOy3LzC0U69rrf/7nf9L5559fdOKObLHNNttsvvYXKycPPvhg0e0tttiiXSf68NJLL6VqFe+V1mWo4j0JAAAAAO01uwDCZZddNl+O3Xr+N0T57/aKMtRvvvnmPPddTToy/9k6sBj69+8/z3VtKa1O17cuiKBoBJULRUJBoX333bdNlTpav6bVOJ/c+jFGYH12bTnmp969e6dhw4ZlrQoef/zxLPYT8Y5CEQeKNtkdEQlEkajw61//Ojt3RhXt1lUMohVNJIRVK/Gk2o4nSR6oMZFB1boNQHwBvP7661OpRV+ZqIoQlQMKRQmayBRrrx/84AfZF/LowZIXXwrij8rbb7+dak2+NFNeW0vLFP5nIV6LarbpppsW3Y6qGAAAAADQXtG2YIMNNihaFyWxn3322W4/dvT9bn3le2EVhLaKq2pbl+7+2te+lqpZBCLbq/VVz5FgES0J5jX3GC19ozJvdyvlldHlcFV2BDzXX3/9onXXXntty++jR4/OStW3t2XB7F7T5557ruSB9a620UYbpV69epX1vHnEOu6888605pprFq2PatddIdplRxWC1sHirtp/ORJPqu14kuSBGnTooYfOkiEaAfvZ9XAphdZ/mKN9wbvvvtuhfUVVg2jLEOVD8iJxID7UrbMLq13r3lvt7XEWvYKiHFA122abbWb54z9hwoSSjQcAAACAylXYVjU/zxlXQU+bNq3LjhEB6NkFbDfeeOOidSNHjmz3vmM+sPXFX+0NIFWam266qd3b3HDDDbMEW2dnlVVWSYMGDZrrtt1hwQUXLLo9u17x1XjsucUcImYQPd3ziQTx2cyLAPS6667bpv1GnKHwMUaF56iyXE369Okzy/lkfrxv2ysSHKJFRaF33nmnS8vU77rrrt22/3IjnlTb8STJAzUoSrpE+4JCb7zxRvrTn/6UykF7e0rNS5QYii99hSe3Dz74IKtAEOVsakW/fv2KbrcneeLzzz+vul5NcyonV9gTLk70Z511VknHBAAAAEBlikBT61LaUXnguOOOKwpWdlRUXY32rbOz9957z1Ja/+9//3ub9x1BsdbJA9HPubDKazWKMtvtKUUez2nredZ99tlnjvc/5JBDim5H692mpqbUnSLpY14JJ9V47NYxgsIgf5S0v/vuu7PP4R/+8Ie5tjmYV6Xn1q/3r371q+yq62rS+n0b54ZyLEnf1bGl+b3/ciKeVNvxpOr+S88cHXnkkamhoWGW6gP5bLuu0NHWAK17qYTWGZnttdtuu6Vbb701+2OeF1/KNt9886w3TS1YffXVi25HGZ/4kjQv8QXq+9///iz9zarR4osvnn74wx8WrYs/cn/96187vM8oUxV/LAEAAACoPVdccUU251TooosuyqrDdrQSbARgIxh6xBFHzLGKQSQPtJ7/jTavbWkPG1dPH3744bPMacX2tSAe57hx49r0Ohx77LFF6xobG2e5OrlQzD0WBpvGjx+fzV13Jtg8ZsyYuf774MGDi24//fTTaX4p5bELRRuPb37zm0XrouJAxCLeeuutlnVRwbj11evz8vOf/7yo8nHMox9wwAGdqvTc3Qkl7fWd73wnrbTSSkXniHifdyYZZE7v2zhHtb7qvaOxpdnFleLC0o5WwGjL/quFeFJtx5MkD9SohRdeOMtwbZ051JXlZtZbb7208847Z6U62lqKKxIOjj766Fn6aLX+otsR22+/fbr99tvTIoss0rIuvgRutdVW6eGHH07Vbtttt009evRouR1fSPfaa6/sC+rcTlSRaXn11VenWnH88ccX/WGMhJrIqj799NPbVWYn+pX99Kc/TQMHDiz6AgoAAABA7VhxxRXTjTfeWBRcDDHfNnTo0HTHHXe0eV9xVWMEJqL8/bzaEMRV1qeeeuos1QSizHL0eJ+TSZMmZcHTf//730Xr4+rqtdZaK9WCmMvbYYcd0ocffjjH+0QAMu7T+rmMOcTW/eFbB5t+97vfFa175JFHsjnwqErRVhFcuvnmm7Oy+RGom9c8faHoQz6/Suu3PvY111yTXnvttVQOrQsiwHfBBRcUrRs+fHiWANIeQ4YMmaUCSDy/W265ZbvmhePzfdVVV6W111677K5ejrjCiBEjiuILr7/+evb63nvvvW3eT8SJ4py34447ZrGj2YlYTcypn3jiie262j3eW3GuLbTTTjvNcr/43Cy//PLpzDPPTO+9916b9x+f7ccee2ye+68W4km1HU8q/sZCTYkvFWeffXb69NNPi0rqfPvb3+6S8lPxIbntttuyJTL74g/ChhtumNZZZ5203HLLZeviOBHAf/nll7NyUHFSaZ1V1ro3WGdEpYG77ror+2KXP8lNnDgxbbfddlllgkgkqFbLLLNMliEYGZV5jz76aNbD6Uc/+lH2xyBOTHFCe/fdd7M/4pdccknLl+So2rDLLruUZT+jrhTJJfFeiC/s+c9GZImecsopWVZ4ZI1uttlm2R+EKN0T7+H4YhfZoC+88EIaNWpUljDTni82AAAAAFSvmHuMgFVUAyi84jXmkiJYGfNM8TPmJgcMGJAFL6PPeATyI+Af801RYj3mT9sTjIiLtGJutjBB4bnnnktrrLFGVpk2Si5H4DPavcYVpXG/Cy+8cJaAWswZRuCwFkRA8x//+Ed64okn0le/+tVsDj3mRCPYmL/4LeYO43lq3ds65gyjYkNbSsBHosDvf//7ovdCzJvHHG0cb6ONNkrLLrtsViY9EgXiWBFQiu0i2SDmuPPVCvbYY4+5Hi/mOaMCQGF/9ghurbvuutkSlRAKg4Shq0qOf+tb38rm9/NtOiIWEO+/aCm82mqrZe/zurq6omSbtjyHHf0cxnP60UcfZbfj+fvLX/7S4ZYFhU477bTss1XYGuSBBx7IEn3iCv34fG+wwQYtn+0IskZ8IgLw8ZrGFe333Xdfy0WYrXupl4MYUyQ1RLA0L+IIkSQR79d4T22yySbZOSySZOJcFe/beN/Fc/P4448X9YRvnVhSKOblf/vb32ZLfA7jwtC4f7x3llpqqawdRpxLI4knKktHG4U4R7auCjCnKhLxHjjppJOyJV6XeGzxWfjKV76SllxyySxGkI+TRMJAxK2efPLJon3EeOLzWq3Ek2o7niR5oIbFF48owRR/2PIiiJ//ItuV4kNz/fXXZ0t7RHWEOMF0pfhD9p///Cc7ueXL6kTCQmSJRUZgJDlUq/hjG4+98D8A8R+DONnPTXyBi8y9cuxj1B2iBFNkTEbJsML2G3FCP+ecc7IFAAAAANoqAogRIIx519ZXq8ecWyztnXOKIGQEJebmT3/6U1auvbDcdsyFxtXvra+An50IBEZQqLDUfrW3mYggUAQ8I7gbF9vFMi8R4Ix59cJA+Nyce+65WXWI1q95VHxoXfWhsyJYFTGA1lfeRwWCWGanq5IHYp71wAMPzOaW8yJAfs8992RLa21NwOiISJDYf//953hVf1Q/7mhsIF73CJJ+97vfzQLZeRFEjASF1kkKlSriCPFY4wrpaF2QFwH21lfld5UXX3wxW9ob0I1zXyQZzEskBbRODGhLYD1e59ZJN9VGPKl240naFtS46MkUSQSFfvnLX7ZkAnZGZKx2pq1CfKD+93//N3WHyOK8//77s0zDvMiQig/3/CrZVApLL710lsUUGZxtFe+P+OIbJWlqSZSHiqzFKMnWmUockYEY2YoAAAAA1LYISseVhlGOOwL/HdW7d+8swBpXLc/rSukI+kcwOgLHbQ1s58VVxHGVewTGa0VcGR5BoLgqvq2+8Y1vZIHw9rTejaBjVAWOede4erejYu524403nuf9IoAfQfO5tVToLnH1bcyxloPWCRSF9t133049P3GldVw8GY+3M/PB8T6K+EW5igs+4yr/uEq/M+ewqD4xO519j8YV3hH7ifNXV8etQnze4ry46qqrpmonnlS78STJAzUuWge07okUWVxdkQkXZZRuueWW7ItsnLDb8uU0Mll/8pOfZCU6CsvfdIcYU5QOivI1hVmP0bbhuuuuS9UqHnecxOJ5bp040jo774gjjsi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      "text/plain": [
       "<Figure size 2220x1380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: E:\\불평등 연구\\데이터\\73_AI_성장\\Appendix_Figure_C1_policy_priority_full_tertile.png E:\\불평등 연구\\데이터\\73_AI_성장\\Appendix_Figure_C1_policy_priority_full_tertile.pdf\n",
      "\n",
      "Appendix Figure C2 predicted values (Industrial Support, full tertiles)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Mean</th>\n",
       "      <th>CI 2.5%</th>\n",
       "      <th>CI 97.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>High</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>5.510</td>\n",
       "      <td>5.424</td>\n",
       "      <td>5.595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.477</td>\n",
       "      <td>4.391</td>\n",
       "      <td>4.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>5.077</td>\n",
       "      <td>4.998</td>\n",
       "      <td>5.162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.478</td>\n",
       "      <td>4.395</td>\n",
       "      <td>4.566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Middle</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>5.129</td>\n",
       "      <td>5.041</td>\n",
       "      <td>5.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Middle</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.513</td>\n",
       "      <td>4.428</td>\n",
       "      <td>4.600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Wealth            Frame   Mean  CI 2.5%  CI 97.5%\n",
       "5    High  Competitiveness  5.510    5.424     5.595\n",
       "4    High             Risk  4.477    4.391     4.556\n",
       "1     Low  Competitiveness  5.077    4.998     5.162\n",
       "0     Low             Risk  4.478    4.395     4.566\n",
       "3  Middle  Competitiveness  5.129    5.041     5.212\n",
       "2  Middle             Risk  4.513    4.428     4.600"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Appendix Figure C2 predicted values (Regulation Support, full tertiles)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Mean</th>\n",
       "      <th>CI 2.5%</th>\n",
       "      <th>CI 97.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>High</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>4.544</td>\n",
       "      <td>4.452</td>\n",
       "      <td>4.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.930</td>\n",
       "      <td>4.840</td>\n",
       "      <td>5.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>4.517</td>\n",
       "      <td>4.434</td>\n",
       "      <td>4.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>Risk</td>\n",
       "      <td>5.349</td>\n",
       "      <td>5.266</td>\n",
       "      <td>5.432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Middle</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>4.502</td>\n",
       "      <td>4.414</td>\n",
       "      <td>4.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Middle</td>\n",
       "      <td>Risk</td>\n",
       "      <td>5.304</td>\n",
       "      <td>5.225</td>\n",
       "      <td>5.390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Wealth            Frame   Mean  CI 2.5%  CI 97.5%\n",
       "5    High  Competitiveness  4.544    4.452     4.635\n",
       "4    High             Risk  4.930    4.840     5.021\n",
       "1     Low  Competitiveness  4.517    4.434     4.600\n",
       "0     Low             Risk  5.349    5.266     5.432\n",
       "3  Middle  Competitiveness  4.502    4.414     4.591\n",
       "2  Middle             Risk  5.304    5.225     5.390"
      ]
     },
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    },
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A5A9/+IN1ZbBDDjlEjjzySLf2r7aNjIyU+Ph4yc3NlZkzZ+pCEAAAAACMRGt7p2wurZCi0grZWFxmDbOoYIvqbDIU9V5Pwi2x0WbJzUqR7eX+uRAgNDRECrLTpDAvU6YWZMnU/CwdaFEhm7gYitMAgodaMKWpqUlfSFVXV2cNqzgara2tQ55n+vWvf61DGO5KSUkZs+EWs9ncH1pJSpL4mBiJDQuT6JAQMXd1SURbm5gaGyW0tlZ6q6qk9bvvdGilq6XF2mVFjWDj7YCLo2CLEhYZ6ZX9Awhss2fPltdff936ePXq1X4JtwAA4K6IiAjJzs7WwxEVfqmqqrILvVjuqzGSv4l8Zdz48ZK2zz56OPpbs7WqymnwpT0qyuE+uyIi9GhMSNABFwnQcMtwOhoapGr9ej0cic3Odtr1JXbcOAkJDfX7nAEAAAAAQOAzNNxy+OGH6zFUuMW248ott9wiY8Evf/lLSUhIcGub8ePH+2w+AAAAwFjV0Nymu69YOrBYQiylZbs97i6ptj/qwEKPti3Mz/R6uCU8zCSTcjL0vlWIpTAvS9+q58yR4V49FgB4U3d3t9TX1w8ZVlFDBVq6uvq7Z42E+r6v9pWamuqT8ElbW5seUU4ugBppeMbb1Lkr3WUlNlbizGZrl5XIjg4Jb2kRU329hNTUSGdpqbR88YW01dRYt1VrGztf33h08FbAxVmwxZyUJOGxsSPaN4DgMGfOHB1usSwEpsItAAAEa/hF1bSd1bU7Ojqs4Rfb0Ivlsfr7z98yMjKcvqZ+NsdkZOgx7qCDBr2+5qKLREpKhty/eYjOL8PZstdeahL9XWD2dIIxt7eLqXfohXf8pXnXLj12ffLJoNdMERESn5fnMPiihvp7BwAAAAAAjE2Ghltc4ekFY8HsxhtvlLy8PKOnAQAAAIwZ1XVNsrF4cIilrNr7RXN1DE+p7invfPatR9tGRYbrzis6xLKnC4u6LRifpgMuABAoVMDDUUjF0nnF8rihocHv5418GW5R1MflbJXjoSR56cIf1ZVGzTUpMVHio6IkNjxcotUFV93d/V1Wmpr6u6xUVkrHpk3SUlkpvXuCQ+pfQvW9cd77ZuwZacDFWbBFyZg1y3qhO4DR37nFQv3cW7t2raHzAQDAVyIjI2XChAl6ONLe3m4NvDjq/OKL8MtQ4ZahqJ/Zak7DWXD33bJXVNSgri/qtnuY4MvO7GzdAWagiI4Oa9jF0W1oAFx/0dPZKXVbtujhSGRiol3wxTb8Ep+bK2FmukoDAAAAADBaBXS4xbZTy2GHHWboXAAAAAAEN1VU3lVVPyDAUqZDLbsbWvw2D3VsT6lAynASYqPsOrAU5vV3ZMnJTJbQ0FCPjw0AI/0e3NjY6DSsYum+oh63tgZuPELN0RNxcXESHh4+bAcZT8Mtw3VuUd1gdJeVmBiHXVZCGxoktLpauisqpOXTT6Wrudm6bY+IqJ+S/vtJObp4GnAZKtiiJO67r/XivcTExBHOEkAgO+CAA+weNzc3y6ZNm2TKlCmGzQkAACOYzWbJzc3Vw9lCCQM7v9jeV3+Tuisra/hzcY40NTW59LftpAMPlPz8fId/Q7dWVVmDLtbgy57wS11ZmcNgi9IZGalHg5NjRqqQi4Pgi3nPbSCEXzrq66Vq/Xo9BgkJkdhx45x2fVGvhXAOFAAAAACAoBWw4RZ1wueUU06xPo6JiTF0PgAAAACC2wPPvy9X3fui0dPQoRpPqU4rFunJcYMCLCr8kpWawEruAPxKXSBUXl7uNKxieb67u1uCnfpYPKG+L6vuKupCK2+GZ9QFT+11ddJSUSGH77+/hHd3/9BlpbFRd1npq6qSzvJyaaupURtYt1X/GsH/LxI4ukwmCe9RUaCRB1yGC7YoD332mTQvWqTvL1++3O35AggeKpxYUFAgJSUl1udWr15NuAUAAAeh/qHCLyps4qzri7qvrk8YKD093aO5uNK1RcnMzHT6N2RMRoYe4w46aNDr27Zulf9deqlHc+swm/WodxSS7+vTiyA46/xiDoTwS1+fNO/apceuTz4Z9LIpMlJ3d3HU9UU9Z2ZxAAAAAAAAAlrAhlteeOEFufjii62Pr7nmGrn77rsNnRMAAACA4DUpJ0MCQXVdk9TUN0tqYqzb2+43eYJ8/MT1OsyS4sH2AOAL77zzjjz++OMyFnjaucVycfJw4RZLeKartVVaKiulVXVSsR3l5XaPWysrpaezU29j3rMP1Rtm6P4wcJW6KComK0tiMjMdjl//+c/SEREhnRER0msyyew1ayR5TzcVTwMurgRbahMTpTmW3wOAsWTOnDk63GIJsatwy9lnn230tAAACCrR0dG6S4qjTilKS0uLNeiibhsaGiTCSXeU4ah9DCchIUEHcjxRpRYv8IWQkB/CL45e7+sT88DwS1ubDr3o244Ow8MvPR0dUrdlix6ORCYmOu36Ep+XJ2GRkX6fMwAAAAAACIJwS3V1tV59UlEFm1NPPdXoKQEAAAAwQGt7p2wqrZCiknLZWFwuv/3F8RJldr+wrDqbBAr1sRy2/95ubxdtjpBD95vokzkBGP3UeRbVZUUFNCzD0lWlvb1drr76ao9DG2PFSMIt47KypKGuThKioyU2IkKiVSBlT5eVsKYmCamtlaqbb5YHzj9fOhsbvTpv2AgJkej0dKeBleg9t7FZWRIRHz9kN7SGf/zD7nFJXp4kf/ml0/cPF3BxJdhiOQ6AsWX27Nny/PPPWx+rcIuvLVq0iO4wPl70IBCkJcUZPQUACBgxMTG6W5oaI+VKuCUjw/OFeFztDON1ISHSbjbrUefo5d7e/s4vA7u+7LkfGQDhl476eqlav16PQUJCJHbcOB14iR8QfFFhGPV3YkhoqBHTBgAAAABgzAjYcEvogJMCzlZQAQAAADA61De16tBHUUmFbNS35fp2e3mtNfiunHb0LJkxaYLb+5+QkaTDISosY5TwMJNMzs2Q9g7W1AfgP9u3b5ebbrpJh1i6u7sdvkddwH/llVeKyWRye/9jOdyifj51NDQ476xicz+8ulr2dXARD51WvCMyIcEunOJsRKWmSmiYb06J1qSmSnlGhmQNcaGZs4CLq8GWssxMfRwAY4sKoHoaQvXUzJkz9YBvpB99nQSCvjWPGj0FABiV9t57bzn55JN1CMXSDaa1tdVr4RZXwjNG6AsNlfaoKD3qkpIchl9UdxdzW5tEt7frW7sOMB0d4nx5AT/o65PmXbv0kI8/dtjdMyEvTwdf7Lq+7Lk1JyYaMm0AAAAAAEaTgA23DLwwwtOWvAAAAAACh7oIWK1QawmwbCwuk6LSCt2RpbymwaV9qPd6Em5RAfrC/ExZWzT8haMjpUI06liFeVm6Y0xhfpZMzc+SguxUCQtz/8JxAGNTW1ub7qyiAhUqmLL//vtLfHy82/uJjo7WHXKH+/6sjpHqwUXzozHcEhYWJonx8brLSkx4uLXLStK2bfKfk0+2C7D0dHQYPd1Ry6Q63DgKqWRl2XdbyciQcAPOHS5dunTQc+21tfLyoYdKa3m5ywEXV4Mt0VlZcsMnn4h5FP6fAwAAAEaT/fbbTw/bv7mbmpqsQRd1m5WVFXydW7wQfmmLitLDWecXswq72HR+yaiqktiWFgkE6u//2s2b9XAkMjHRadeX+NxcCYuM9PucAQAAAAAINgEbbpk8efKgEzQJCQkyFnR0dMinn34qGzZs0Bef9PT0SEpKir7AZNasWXqlFwAAACCQqYLtzsq6PcGVMrtuLLsbRlaMVPv0lAqbeDPckhgXrUMrKsjSf9sfZlFdYgZ2owQApbe3VxobG3WQxBJcGTjU8+p1FW6xdffdd9tdHOOqRBdXDlXHHu3hliizWRLj4iQuMlJiTCaJ6u2VyM5OCWtpEVN9vYRWV0tvebn01tY6XC1WrbP7nQHzHlVCQiQ6Le2HYMoQXVbUhUGqq1Cgcvh/KzFRFv33v/LC4YdLe52jy7X62YZZXAm2mJOS9H7TCgo8nzAAAAAAQ6i/a9RiFWp4o9YfrOEWl8Iv0dF6WMS0tARMuGU4HfX1UrlunR6DhIRIXHa2DrvYhV/23MZmZUkI55MBAAAAAAjccMuBBx6ou7W0t7frx5988olM2rOa4Wg3ffp06e7udvq6WsVl0aJFcu2110pOTo5f5wYAAADY6unplZKyGh1aUR1VLF1YikrLpbnVNyvZq/17SgVPPJGREi+FeZn9XVhsurFkpsQH9EW3APynq6tLB1IchVVsh3rPUH/zD0Vt74nw8HC9YEhDw9AdstTcPBEXF6ePoT4Hhl0kFBsr8dHREmvTZSWivV1MjY1iqq2Vvqoq6S0rE9MQn/s+9XPNsk+/zX70iIiLG9RRxVF4RQVbQsMC9pSkV6Tts4+c/uGH8tKxx0pzWZnT97kSalFix42Tn77zjt4vAAAAABx77LEyZcoUu04wagHN0Uh1cPFEb0iIlObminlPB5jotjaJ6Ow07u991b1n50495OOPB71sioyUhLw8h8EXNcwuLlwCAAAAAECwC9hKcmRkpA5wPPXUU/oihSVLlsgFF1wgY8FwF7mUl5fLAw88IA8//LBcc8018qc//UnCfHBRQFVVle4c4+uLbAAAABAcymsa5NMvv9vTgaW/E8vm7RXS0enZRdqeUsEZT6nuKkPJyUy278Ky5zY5IcbjYwIIbqp7ylAdViyBleGCI94wkr+7VXeV4eaoPh5PqPM2av/eXjlWnetIiImROLNZYk0mMe/pshK+p8uK1NSokyQS2tQkoX19Q89RXSji1dmNDaHh4cOGVfRzGRkSETP2fla2DnHeTH1eTnv/ffn45ptl68sve3yMvU89VQ67/XYxJyc7PZ4KDAHAaDIWahNVy++xdvWrbVS94Vw3ddGtg57buHTwc0NJjo+m2ygABLGTTjppUBftetU1pLJSD3U9geW+JfzS2dkpwajwsMOkvaREGktKpHvPwqiuaDObZevEiXbPhfb0iLm9XYddVGhGDbXowHDnFPyhp6NDajdv1sNZN8+BgRfL/fjcXAmLjPT7nAEAAAAAGFPhFuX3v/+9vPjii3qVkU8//VQWL14s559/vtHTChhqRdQ777xTPvvsM3n99df1Kqze9NBDD8kf/vAHr+4TAAAAwWv5Fxvl3FuXGD0N2bqjSrq6eyQ8zP3LlFVQJTQ0RPYan/ZDB5Y9HVmm5GVKbLTZJ3MGEFjURYSNjY0OwyqW7iuW4Iqlo2wgGGm4paSkxGf7T0pKcjncEmU2S3xUlMRGREiMpctKW5uYVEhFdVmprJSQmhoJ7+6mg4qPRKWlDQqoOAqwqItn6FDm3EPp6T4/hgrGDBeOuS4ALsQCAG8aC7WJtKQ4fVtd1+QwrOIud/ehwjWWOQAAgp/6u039Xa6G6ugykAq/qPMdlqCLJfRi+9iobqxDiYqKkjNefVV/fOpjaK2slPriYmkoKZEGy21JiX6ueedO6evttW7bFhU1aH+9JpO0xsToYQm75G/fLsGgva5Oj8p16wa/GBIicdnZ9uEXmxBMbFaWhBBqBQAAAAAEiYAOtxQUFMiDDz4ov/jFL/Tjiy++WJ+0GI0dXNQKWXPmzJETTjhBDjjgACksLJSUlBTdwUadaCouLpYPP/xQ/vnPf8rWrVvttv344491l5u33nrLJx1cAAAAAEUFQAKBCrZs21mtwyjumjghTVo+/ruYI8N9MjcAxlIXYljCKUN1WVG3PT09EmxGGm7xxf7VeZrOpiaJV4EV1WUlMlJiTCaJ6uuTyI6O/i4rqmOM6rJSUSHhbW0SZnOxCbwnPDZ22LCKuqBFBVtM4fwcBAAAAICxwtJxVY2pU6c6XAREnS8ZGHqx3KquakaEXzIyMqwLLqhby9+42QcfPOi9PZ2d0rhjhzXw8sHKlSI7dgy5f9XFxdPlHOoTEqQuMbG/A8yeTjDhXV3GLNLR1ydNO3fqsfPjjwe9bIqMlIS8PIddXxILCiTSy4uoAgAAAAAwEgGfhLB0arn00kt1B5cLL7xQnn32WbnyyivluOOOk/BRUIy//vrr5ZJLLpH8/HyHr6enp+sxd+5c/d5HHnlErrnmGv35sFi+fLncdtttegAAAACWC47VKrBFJRWysaRcikrK9e3Tt10gWanuF6w8CZP4ivpYPJmPCpWbI1mlDhhNdu7cqVf2VsEM1Y1lNPNnuEVdFNJSWSktFRXSUl7ef7tntNrcV6O7rU0SReQgj2cHZ0LDwuzCKU7DKxkZEhEba/R0AQAAAABBSJ0zVQtvquEs/KLOGQwMvdiGX7q7u70+r8xM18//miIiJGniRD2UtWpR0GHCLSmxsZJcWCiNJSXS7Wbn3pqUFNlWUGA/h54eMduEXWyDL+rWqPBLT0eH1G7erIcjqmuro64vKvgSn5urP7cAAAAAAPhLQIdbbDu07LfffrJy5Up9gd4HH3yghwq2TJ8+XbJVi9WEBLe7lqjVPVQnFKPdddddbp1YUkEf1dnlxz/+sd0KKX/961/l8ssv10EYb1DHUR1hXKVOaM2bN88rxwYAAIDr1O/IOyvr7AIslkBLbUPLoPd/u63Mo3BLbLRZcjKTZUeF5xdXj0RiXLQU5mfK1Pwsj+YPILCoCyMaGhr0yqCqq4r6OzfWg4vzVcfT0tJSGQu8GW6Jioy067Ji7uiQiI8/lsXTpunASrubxzJkZdIgFpWS4jiwkpVl91hdYBISSigTAMYq1dFddbgP5L9Fv//+e8nJyfHqfqlNAAAQWNQ1CqmpqXqo6zMGUt1x1c9jFXQpLy/XtwPDL5500HUn3DKQOu5w9j3+eLngqqv07zTqXIju+lJcbO3+Ur/nftP33+vuKLbazOZB++sxmaQlNlYPR0zd3XZhF9sAjArFhHd3G3J+pb2uTo/KdesGvxgSInHqepwBwRdL+EWdu+C8BQAAAABgzIRblixZYm0zq1juq5MLSmdnp6xbt07Wr1/v9r7VPgIl3OKJI444Qm6//XbdycWipaVFd3X5/e9/75VjWDrGuKq6utorxwUAAIBjPT29UlJWIxuL7UMsRaXl0tz6Q1e/4ahtjzqw0KM5qHCJr8MtGSnxOsCijlWYlyVTC9T9LMlMibf7+wBA8Hj++ef1xQ3qQgfLUKEW2wsb7r77br2whbuSkpJkrHAWbulsarLrpDJwqE4r1TU1cmBzs0S0tUlkR4eYensd7mu3jz+G0SwsOlpi94RThuq0Ep2ezqqno8SlVVVGTwHAKKdWTr/44ovld7/7nb6YNJC89tprcvPNN8tpp53mtZqEBbUJAACCi8lkkrS0ND2chV/Uwia2HV8s9y3hF7UIykAZGRkez0nt39XwjDrnrP6eVyP74IMHz7+zUxp37LALv2z46isRN7vV9ISFSXNsrB5Owy8Dur3Y3qrwi9/19UnTzp167Pzoo8Fzjoy0hl0Gdn1Rt5EJLFIFAAAAABhF4RZnuKCt3xVXXCH33XeflJWVWZ975513vF5IAgAAgH91dnXL1h1VP3RiUWGW0grZvL1COjpHXsBS+/KUCp389/ON4g2qC4wOruRlytSCcfpWhViSE2K8sn8A3qMWiFAXGaiLFTzx9ttv2/3t6s2uJKqLq+rmqrrAjFZhJlN/lxURefeyy6StslIHVizhla6WwV26HEn0+UxHnxCTSWIyMgZ1VXEUXonwoPMQglt0WprRUwAwyqkFvv7xj3/I4sWL5cILL5Srr75axo8fb+jvhMuWLZM777zTo0XHAADA2KTOJw0VXlXhl5qaGrvQi7qdNGmST8MtroZn1AIVSRMn6mHx6M9/LuLlBQ90+CUuTg9Hwrq6rEEXc3u7RNt0fdHhFw+644x4zh0dUrtpkx6OqI60zrq+xOfmsvgHAAAAACD4wi2WLi0YLDIyUhYsWCCPPfaY9blVq1ZJmzp5ERVl6NwAAAAwvJa2Dtm8vVI2FpfpDiyWMMt3O6t1lxZfUWEZT6nwiTtCQ0Nkr/FpezqxZFk7skzJy5TYaLPH8wDgHSqwokIhlm4qahVNS1cVy33L40WLFsk555zj0XFUdxVfhVuU5OTkoAy3RIWHS0xEhA6tmHt6dFcVU1OTmGprJWT3bons7JSIjg69MqdlmY+vDZ7zaGFOTrbvpuKky0pUSoqEhIYaPV0AwBjX3Nwsf/vb3+TBBx+UhQsX6qDL4Ycf7rfjq9XUlyxZomsRJSUl1toNC5EBAABvhV9U0ESNGTNmeCUg7Mp5JkvnFnd1d3frMI6/dYeHS5MaLoRfHHV+CTMg/NJeVyfta9dK5dq1g18MCZG48eOddn1R52U4JwMAAAAAY09Ah1tuueUWo6cQ8A466CC7cIta1UStZJKXl2fovAAAADDYN9/tkiff+HxPJ5ZyKS3bbcg81LE9pcIpjkSEh8mknPQ9nViy+m/zs2TvCelijgwfwWwBeFrEtwRU1K1tcMV2qNdUwMUVIw2f+Hr/lgstjaYu8YyJjJRYk0mi+vokUoVTWloktKFBQmtqJLytTYdW1PMmFz/3cE1YVJRdd5WBwxJgiU5Pl7DISKOnCwDAsFSX9rvvvlsvaGX5He+5557TY+LEiXL66afLT3/6U69cBDpQfX29vPbaa/LSSy/pjvHqIk7LYmSWUMvs2bP18QEAAAJJe3u7HHnkkfq6CTVUEMXRoqqehluqq6tdPp8WSOGX8M7O/k4vTgIwfg+/9PVJ0/ff67Hzo48GvWyKjBwUfLENv0QmJPh3vgAAAAAAvyDcEuQcte5VJ1MItwAAAASe7ytq5d5/Lzd6GlJV2yS765slJTHW7W1VYGVWYY5MzR+nO7BYOrIUZKdKWJjJJ/MF0E8V4VtbW4ftsqJGU1OT14+vjuGplJQUw8MzIxUWGiqxqstKSIhEdnfrLithTU0SWlsrYY2N1sBKeFeXhNKF1mvUCp3RGRlDhlVi9wRawmNjWUEeADCq3HrrrXL++efLtddeKy+//LLd74Vbt26VP//5z3pMmDBB5s+fr8cBBxwgU6ZMkbAw98o/5eXl8tVXX8lHH30kK1askLVr1+pAi+V4ivo5q+6rusTtt9+u5wYAABBo4uPj5de//rX1cVdXl+5Ep4IuFRUV+lZdU5GYmOjR/tX2wagrIkKPxvh45+GXAYGX2JYWSa6rEyP0dHRI7aZNejjryuus60t8bq6YIiL8PmcAAAAAwCgPt2B4jlYY4UIOAAAA3/7+pUZoaKjb26puJoGiqLRCDt1votvbJSfEyJqnf+OTOQFjlVrpsaGhwdplxVGHFcvo6OgwbJ4jCZ8kJSX5NDwzknCLOSxMYkwmiVZdVjo7+7us1NdLaF2dDqvo0dkpYd3duisLvMOclGQNpwzVaSUqJUVCTYQnAQBjV25urixbtkxWr14tv/3tb2X58uV2QRNlx44d8vTTT+uhqGCL6uySk5Mj2dnZOmgcFRUlZrNZB1ZUJxgVhi4rK5Ndu3bpoIz6PdSWbaDFciz1O9cNN9wgl19+ud4fAABAMAgPD9e/E6nhDSogMxpZwy82HVGS6upkztq1Eojaa2v1qHQ0v5AQiRs/3mHwRQ113kktqAIAAAAACDyEW4Kco1VB0tLSDJkLAADAaNLT0yvFu6qlqKRCNpaUS1FJub7dVFohn/7zBtl37/Fu7zMnM1mizRHS2t4pRttYXOZRuAWA6zo7O4cNq1g6r6iAS6AbeMGjO1wJn4xk/wlxcXaPVQglJjxcokNCxKy6rLS3W7usRLS2SsSeLisqtGIKgs99sAgzmwcHVvZ0VbELraSn6/cCAADXzZ49W/773//KqlWr5N5775VXXnlFB1VsF7uyBFLU6uRFRUWyyckq18MtomUbaFEjPz9frrzySvnFL34hsbHudwBF4Lnl0dclKzVBEmOjJCE2ShLjovfc/vA4Psbs0cIeAACMdup6DNUxz9IFZiTntAKd6uDiqdrEROkxmaxdYPx6Dq6vT5q+/16PnR99NOhldV4qPi/PGnwZGH6JtAn4AAAAAAD8i3BLkPv888/tHptMJklPTzdsPgAAAMGmo7NLtu6o0p1MVODDEmbZsqNSOjq7HW6zsbjco3CLuihkSl6mrNu0Q/wtNytFCvMzZWp+lhTmZ8kRB0z2+xyA0eq1117TxeyBoRW1GvZooj4mFcLx5AI3tVq4K/u31dvdLa1VVdJSUeFwtNrcr+/u/n/27gO8rer+//jXWpb3XpnOgCzCCBAIe7RAKaOLMlpm2fxoy6b8+mOVf1toS0vZFAqlbChQRkuhQJgJAUoTIANI7CzPON6WZA3/n3NsKZIs2daVbEn2+/U857m6ku65Rw4k1j33c76yKDtbh1VUaEWFV6iykhhqFUsVRvFXUhmq0ootP59qsgAAjLLFixfLk08+KVu2bJE///nP8uijj+rKK37h/xZHqv4e/t7wgIxqmZmZcswxx8jpp58u3/zmN/k3fpy5+5m3RvQ+FXDxh10CwZfc0CDMOd8+UL8OAMBEseeee+rm53Q6dcjF3/yhF/+2ra1NJmK4ZUN1tbSUlgb2bS5XIOji3xa3tkpOT4+MNY/TKdvXrtUtEntxcdSqL/nTp4vZZhvzMQMAAADAREG4JY05HA556aWXBq3elpWVlbQxAQAApKpuh0tXXfFXYNEhlg31sn5rs67SEos1tfWGx6ECJqMVbjGZMmT2lPL+EMvMKplXXaW3c6ZXSG42K+QDo+WZZ56R+nrjfy+kC6/XKx0dHVJYWBjzsUVFRUO+bjebxepwyFNHHCGOxkbpqq8Xx7ZtepXFkVC30mXHMdk+EakVKMMrq0QKr2SVlorJbE72cAEAQJgpU6bItddeq9vHH3+s5wpeeeUV+eijj/TvbeEiVXgJD76ohbO+/vWvyze+8Q0daClgxeoJr6Pbqdvmxugr0p9y1GJD4ZbNDdvloZeWDVk9Ji/bLmYz1WMAAKnNbrfL9OnTdYt2X0dTU5MOu4QHX1Rrb2+XVHXoZZfJwoICaduwQdpraqR9YNu5Zcuw1+2cYfet9GZm6hb8aeevXp2UcMtwnNu369b48ccRF4LJnTw5ctWXmTP19TSC4QAAAABgHOGWNPaHP/xBX/QIdtRRRyVtPAAAAKmgtaNbB1dCQiw19bKxviVh51ChGKNU5ZR42awWHVgJrsSitjtNK5dMmzXu/oHxRlUEUd+dWlpapLW1Ve+rx2qr9tXjY489Vn74wx8a6r+4uHhChFsU9TOLFG5xOxyDqqkEt+0NDVLd1yfm9nax9fQEqquoraq2Yvb1hwzHvq7V+GLOzBwUTokUWMmuqBArC2MAADDuVg+/7rrrpKenRz755BPdvvrqK6mtrdW/C3d3d+sbK61Wq2RnZ+vf6aZNmybV1dWyyy676OPVYyBWRqu2rNvYKNfe80JM1WNCwi9hFWR2vD6wHQjN2DOt3GAKAEgqtTjpcOGXSKEXfxgmmdWh5x5wgMzfbbdBz3tcLunctEkHXcKDL6o5tm8Xh334BbeynU5D41Kxmq2TJgWqwNidTjGNcJGcePX5fNK5ebNuW94aXA3PYrdLfnV1xKov6rnM/PwxGScAAAAApKu0Dbf85z//kffff19WrFihbyJSNySpL/V5eXl6RdiqqipZvHix7LfffrJo0SJJRepCRFlZmZgNrID66quv6hXZgqkJqfPOOy+BIwQAAEhNanXZxpYOWVOrqq/UBQIsKtDS0NIx6udX5zNKBVFGKicrUwdY/BVY5lX3V2SZMalULBZW0QdG6sEHH9SrWA+lubnZcP8q3DJRvH/33VLrdkt3fX1IeKW3Y/i/e+eMyQjHoYwMyS4vHxRQiRReUdVYuHEPAICJTc0T7L///roBo01VVcm22wwd297lSFj1mOEWCBkqFLP/brPkO4el5jwiAGDihF9mzJihWyQqpOyv/BIcevGHYEYz/FJZWRnxeUtmphTttJNukdTX1sqr55wzbP92g5WY3VarfD5//o4n+vrE7nLp/vyBl+DtWIZfPE6nbF+7VrdI7MXFUYMv+dOmidlm7HcrAAAAABgv0ircolaseOCBB+T222/XK46F3+Do57+R49FHH9XbnXbaSS6++GI566yz9IWBVPHEE0/I3XffLVdffbWccMIJkpubO+wxHo9Hf/6rrrpKPw526aWXRr24AAAAMB68v3K9XHHb32RNbb20diSvVP0XmxrF4/EaCphECrcU5Wfryiv9ARZViUWFWCbJlPJCMZlMCRo1kPq8Xq+0t7fr6iD+5q+2or4vnXHGGaMWPlHnMqqkpETGg8yMDMlSE8G9vWLt7hZzZ6dkOp1iG6iuoqqsbH3jDWkYqLKC+Njy84cNq+j9sjIxWdLq8g0AAAAmCFUdxWi4uq1zbK7r9Lo90tzaqVsk3Y5ew+GWO596U+qa2wNhGarHAABGQ05OzrDhl0ihF/++et0INTdRWlpq6NjWnuH/nVf/Mn7rrrukW1WACar60rlliw6rDGVQVZiMDHHa7bq1RTpgIPyiwy5BwRcVesl2OPR1z7EKvzi3b9et8aOPBr2WYTJJ7pQpgcBLIPwysFXXCvmdAgAAAMB4lzZ3R3z00Ufygx/8QIdagoMsivryFv4FLvg9X3zxhfz4xz/WoZBHHnlE9tprL0kVamwqdHPRRRfJUUcdpVdT22OPPXRJ2oKCArHb7fpGrg0bNsjSpUv1isMbN24c1M8RRxwxqJILAADAeGO1mOX9VeuTPQxxe7yyfkuzzKmOPVg8a0qZ/M/3D9EhFxVoUdvy4jwmJDCu9fb2Dgqr+B+rrdpXj9va2sQXJTgxZcqUlA23qOqhqUr9zZKlQisej1idTrF0doqtpycQVvFvbb29Yia0Eje1smJ4OCVieKWiQqzZ2ckeLgAAABAXFeIwaqSVW0abCqUY9cg/P5Dln9bEXT0mPAyzIyTTv5+fk6Wr5AAAEC38MnPmTN0i6erqihh68T/uiRJEKS8vF7PZWAV51e9wSkpLZbcI13s9Lpd0btokbUGBl0D4ZcMGcba2iiPWRW2Dwi+tEa7lZvh8+hppoNpLWOWXsQq/9Pl8+rOrtuWttwa9brHbJT88+BIUfsnMzx/1MQIAAADAaEuLcMszzzyjgy2qUokKrQTf+Kf2w8MufuHvU0ESFR557LHH5Lvf/a6kWlWa5557TrdYHXbYYfpnZLVaR2VsAAAAieLqdcuXm5pkxuRSycnKjPn4uQbCJKNldU29oXCLCujcfuXJozImYCyp71hq1b+hwir+QIuaQI1XPOGT0Q63jKT/RFPTytl9fTqcoqqsWLq7Q8Iq/scqtEJ0Lk4qHFRaOjioUlU16LnMwkLCigAAAJgwVPDCqLYUCbfE8xnau5wJqR4zEnk59qjhlxvPO05KCnMN9w0AGN9URezZs2frFukar7p2Gyn0UlhYaPicqo/hVFRURHzekpkpRTvtpFskrvZ2eezBB2Xlyy9LovSZTOLMytItWvhFVXnRgRenU+wDoRdV9UU9VlVhxuKKoMfplO1r1ugWSVZJyeDwy8A2f9o0vTAPAAAAAKS6lA+3vPXWW3LqqaeK2+0OVGjxh1mys7Nlt912k4ULF+qbidSKFOrmKnVT0qeffiorV67Uq0wEV3ZR/fzwhz+UsrIyOeiggySdqc9/3XXXyeWXX65LwgIAAKSKbodL1tY2yOoN9bKmtr5/W9Mg67c2i9frk3/f9VM5fPE8QxP5UyuKZHNjqyTbmpp6+faheyR7GEDCeb1eXUElPKASqamKLGNFfbdTiwJkxboq3wjDJ+rzqqoxRr5blZSUSKKo2J/d4xGbqrLS1SWZamVAFVIJC65YvF5CK3Gy5eWFVlOJ0rLKysTMYhIAAABAQquepE7lFuPVZ9o6I690Pxo6u526Rbomdt05xxjqc11tg9zy8KsjqiBD9RgAGJ/UfTR5eXm6RQq/GDWSyi2VlcYWM8ssKJAugxVl4gm/OLKzdYskJPwSXPUlqPLLWFzLdbS06Nb40UeDx2gySe6UKRGDL6qp66As2gMAAAAgFaR0uEXdKHXmmWeKS33RG/gSpYItu+yyi1x11VXyne98Z8gbm5xOpzz77LNyyy23yKpVqwIhF9Wf6nft2rVJrXZy4oknSmZmprz55puyYsUK2bhx47DHqButVJhHVbI5++yzpSjCqhEAAABjpbWjW4dWVBUTFfbo3zbIxvqWIY9T7zESblHmzaga03CLzWqROdMrZP6MKpk3o1Lmz5wk86orZadp5WM2BiAR1PegaCGV4OorKtiiQh6pSI1v8uTJoxJuUaGe9vZ2Q9+xhutffZu1+3z9IZWeHrE6HFGrrJhT9GefLkxWq+RUVIRUVYkUXMmuqBBbTk6yhwsAAAAkxfcOWyROt1sHTNo6HQPbHunoHlklkoRUbhnDYMhQVJAj3QM6Rv8cNmzdJn9+4b2YFp2JFn7pfz5sPy87JDBjz7Ry0ywATBCq6su0adN0BRd1XTqR4ZaRVoZJtfCLP/BijxCAUdeGR/tfyD6fTzo3bdJty1tvDXrdkpUl+dXVO4IvYeGXzPz8UR4hAAAAAKRBuOWOO+6Q2trakGot1157rfzf//2fmEewEoPdbpdTTjlFTjrpJLnpppvk+uuvD1w0Vf2q/i+55BJJlqqqKrngggt0U9RNZCpws3nzZr2ShapC4/F4JD8/X99gNWXKFNlrr730qhkAAABjRf0e1tjSIWt0JZa6kDBLQ0uHoT7V8UapgMmry1dLouVkZfaHV3SIpSoQZpkxqVQslrFdBQyIhfresG3btqhhFX/1FfW+dKc+02iFW/z9B4db1IRfz7Zt0qO+nw3R2pqbpaKqKhBWsUUIrXD7TnyySktDwylRqqzYi4r0KoQAAAAAorvrZ6dIWdHguSZVbbezxxkIu4SHX/S2a8f+vgtnTthgiNvjlR7n2FUzjUYFRjJt1jH5M/BXj9licNEZq8UcMRDzjf0WyI++dYChPgEAqUktNquaml9S98GoMIpq6j4Y/3bWrFnjJtwykvBLT3a2blHDL8GBl6BtXmfnmCyI5HE4ZPuaNbpFklVSIvlRqr7kT5smZptt1McIAAAAYGJI6XDLQw89FAi2qO2vfvUrufLKK2PuR1U7UaEYVeVFVXzx9/nnP/85qeGWSKtX7LvvvroBAACMNfX7kaqIEhxgWb2hXtbU1ktrR2JX01TBGKNU6CQeRfnZuo/5M6tkXvXAdkaVTCkv1L83AunmnnvukVdeeUUmAhXUMUJVzMzJyRk24PPq//6vlLW3B0IrPU1N0uf1jugcu2/bZmhsE5k1NzdiQCU8vJJdXi7mJFZdBQAAACYKs9kUqLYxvapkVM+1eEG1+Hx90talQjPOQHhGVY/xL3g3FtRnTedwTmEaVc9RgaDm1k7dglWVGl8J/ud3PS81dS1DVo8J3mZRPQYAxpT6O1ctJqTa3LlzE9Kn+j1BhWPGEx1+ycnRLdx+y5ZJXgosXOVoadGt8aOPBr2mFhvKnTIlYtUX9Zyqos2/vwAAAADSPtyyZcsW+eyzz/QXHNUWL15sKNgS7IorrpDnnntOli9frvdXr16tz6MqogAAAEwUHo9Xauq2DQRXGgIBFhVo6XZELg+faPFVbhlZuKWqtCBCJZYqKS/O4yI6ksblcoVUWPE3FcRXFSeNGGlVkvFA/ayi8fb2SndjY38oJUJ1FWtnp2T5fGJzOsXmcAyqrqK2bd3d0jXCMAsiM1kserJyqLBKblWVfo8tNzfZwwUAAACQJNecdXTE530+VT3GFVQ5JkIFme4IFWXUvn6+R1y9nlGv3DLWwZBEjz+lAjoGA0bKK8s+l4/XbIqpesyOsIu9f5sTth8ciskNDcmopkJgAIDkXmOfP3++rt7S1NQkbrdbxjNVvcUIt8WiK8WoCjBWt3tUK4urCuidmzbptnnp0kGvW7KyJL+6OmLVF9Uy840HXQEAAACMPykbbvn444/11l+15bzzzktIv6off7jFfx7CLQAAYKJ46+Mv5Ij/uU163SOf5B8NTds7ZXt7txQXDF6FKtbKLWol0f7gSn+QRVVimVtdKUX5sfcNjAb1nePOO+/UwYxolUMmT55MuGUE1r39tqz46quIARbnEMEXZbFaQW7MRjr+ZJWUhARUooVXsoqL9Up9AAAAAGCEqqrrDxEY5XS5dXAjJPwSvq+3Tr0IykQNhqifSfoHdJwxV4/Z1talm1G52ZlBoZj+/1b/csOZUlrIAg4AMBbsdrvcfPPNgVCsuu6uKrmosIt/63+swi8eT3Lnw+Jh7e0Vi8HFmFoLC+WT3XfXj81er9gdDh2UyQraZvf0SH6X8X8TR8rjcMj2NWt0i3btuSBK1Ze8adOo6g0AAABMMCkbblFfNoMdeOCBCen3gAMO0Fv/at3h5wEAABjPplUWJz3Y4rempl723312zMeVFObK4//vbNlpWrkOseRkZY7K+ACv1yutra26qQmyvfbaS8xmc8z9qO8emzdvNlyRZKKGW2wej9hcLl1pJVBZ5T//kbcN/qwItkjEFfNyqqoGh1XCnssuLxezzZbs4QIAAADAiNgzrbpVlIzeKuAq0HD2tw5ISPWYeKhwhVHt4yDckowKOl09Lt22SGvgObPJ2FWH/6zdJFfc9owU5vorxuyoFBOtekx+jl0sltivTwHAeA3FlpaW6rbLLrtEvMavrr0HB1/U1v9YhV/Ue1JVgcUieVOmSOfWrWpl4JiOddjtgcdes1m6c3N1C5bb2Sn7f/CBJJujpUW3hg8/HPSaWkgpb+rUiMEXtVUVwv33fwEAAAAYH1I23BJ+c1d5eXlC+g3vJ56byAAAAMaSqnSiAiGra+rlG/vtIlMqimLuY3pVsWRlWsXhSn6Z9tUGwy3KSUfunfDxYOJwuVz6e0BLS4sOrqit2g9v7e3teuU3v8cff1xPko1G+MThcOiWlRX7DR0lJSWSLjL6+sTmdkumCqyo4MpAaEU3/+OBrSnGyTr0yzCbJaeiIqSaSrRmzc1l4g8AAAAADJg9tVz+9PNTE1I9Rm91KGbH6x3dTukbwffidAuGJLL6jPr5pEpAJz/H2J/DlsZWeePDdQmpHhMIw6h9/fzgwIx/q64Pcz0AwESgFqsqKyvTLVr4Rc0PhIdegiu/BM8RjLV5Bx8s573+unhcLunYuFHaN2yQ9poa3do2bJCOga2rrW3QsY4RzDWoCi5GdeXkiM9k0hVgrKNYHafP59OfXbXNS5dGXMApPPgSHH6x5Rmr0gcAAAAgeVI23JIX9gVD3XQW/pwRbWFf6hLRJwAAQKKoSdnGlg4d/PAHWVZvqJc1tQ36eb8nfnm2nHjE3oZWsVLVTj5ZN3QVidGmqq2oSXogkf/vdHZ2DhlW8bfu7m5D51B9j1a4RVFjmzx5csz9FxXFHnRLNLPXq6usBAIqQY+DAywq2MKtE8bYi4oCYZXcqqqowZWskhK9mh0AAAAAIH2rx6gbaTt7XBHCMEEhmK4e2XX2FMNjTJVgiNGAjlq8x+1J/mr7eTl2MZtNY/pnEKl6TCwsZlNI+OXEr+8lV5x2pKG+ACDdwy9qgVzVFi5cGDH8sm3btkGhF/9+c3PzqIZfKisr9daSmSnFO++sWyTOtrb+0MtA+EUFXr746qthq72oYIpR62fMkAb/+NxuHZRR/dkHtv59HX4Zxeo4HodDWlav1i2SrNLSqFVf8qZNE7PVOmpjAwAAADDOwi3hFVZWrVol06ZNi7vfTz/9NHDzm1qRRq3QAAAAMNbUxe7Nja39wZWBEMuamga9Hcmqiep9Rs2bUTVm4Zai/GyZP6NK5s+sknnVA9sZVTKlvFAHbYDhqMkjFSpRwY/hgitu9+hWJDJa9VEF6q1W67DjMxpuGc3KLVZVZSUorBItwKLCLYRWYmex2yWnqioQTIkWWMmuqNATmAAAAACAiUFdN1PBA9WmVY5s0YxYffvQPWTWlLKQsExweEa1kVSPiVehwXBLqoRzjI4/mdVzPF6fbGvr0k05cPedDPd15g0PyRcbmwZXiBn47zdaBZlsu43qMQDSIvxSUVGhWyQej0eHX8JDL/6tei2e8Eu084azFxaKfY89pGKPPQLPPXfhhSJffjnkcYU2m4j6u9jAv/cOuz3w2GO1SqdqURYXDg6/BIde/I8toxh+cWzbplvDhx8Oek0tEpU3dWrUqi/qujz/VgEAAABjL2XDLXPmzNFb/xeFRx55RI455pi4+1X9BJs7d27cfQIAAETj8Xhlw9ZtOyqx6Cos/UGWHmev4X7V8UapsEmiVZUWyLwZlbpvFV7xb8uL87jwi4hcLpcOc0QLq6jnVZhFVV4cixspRkKNyQj1/4CqrtLU1DQq4RmbzSa5ubnS1dU1svH4fDuqqYRtg6usqOdMKfKzTydqQkxNekUMqYTt2/L4OxIAAACYiJpbO/XWf2P5WPOft6wo8g2ImBjO/tYBQ76ubsZV1UHaBoIuwRVkogViAu8ZOMbpco9a5ZZkBUMSNf6UCujkGf8MahGllV9sMVQ9piBaACY3ciAmPDhjsZgNjxsAEsFisejqKqrttttuEcMvqrpLeOjF/1iFX4aa//BXbjFC9T+c79xzj+yz557SsXFjf+WXgaovwVVgXG1tEY91ZI38346RhF+yo1R9UY9HK/zS5/Ppz67a5qVLB48rKytq1RfV1PV9AAAAABMo3LLrrrtKVVWV/sKlvsw9/fTTcs4558jhhx9uuM+33npLnnzySX3zkOpT9a/OAwAAEC9Xr1u+2NQkqzfUBSqwqDCLeq7X7Un4+VRIxigVQjGqelLJjgos1ZWBSixqYhFQv2N3dnZGrawSHFzp6UmNGxBiYTR8ohQXFw8bbokUnlE/U2drq3Q3NEhPQ4PeRmoZBQVitliGrK7if83mdlNlxYDMwsIhwyq5VVV6P6ukRExmbq4AAAAAEF351y9P6vnnn3C93vZ9dG9Sx4HUrx6Tn5ulWzzXTIcKv6j9ytKCNA+GGL8uqn4O6R7QUX+2RqvHtLR362ZUTlZmIASz9N7LCOwBSMnwi7ovSbVIVLX34PBLeAUYo+GW7u5uPVczksowqrp48Zw5ukWi5ifCgy8tGzZIbwIDJyr80qFafn7E1629vVErv9hHMfzicTikZfVq3SLJKi2NWvUlb9o0MVutozIuAAAAYLxL2XCL8p3vfEfuvPPOQBjlu9/9rrzwwgty0EEHxdzXsmXL5Nvf/rZ+rPpSfar+AAAAYqFWG/xs/daBCiwNgUos67c0i883dhUOvtjUqKvCGFmdbv7MSUO+bjabZPaUsqAKLCrEMknmTK/QE4ZAsJUrV8r999+vq6yo8IeajBmv4gm3lJSUDPueVX//u+S+8sqg4IpvBD/TfUwmMft8hsc3UZkzMyWnqipilZWQAMvAJB8AAAAAABi5TJtVyotVi3yzajwmlRXKTRccP2wFmZFUj4nH+Kjckp2Wn6Hb4dKtrrlNsjKN3UD8zidfyhnXP2SogoxqVqrHAIiD1WqVSZMm6ZZIKhgzEiMJz9iLinSrWLQo8NymTZvkgR/9SMaK22bTbcjwS3DwJWhrdzjEMkpzJ45t23Rr+PDDiBXe86ZOjRh8Uc9ll5dT1R0AAABIx3DLz3/+c3nooYf0qs7ql/qOjg5dueWiiy6Sq666KurqBsHU6si33HKL3H777fpGO/+Xg5ycHLnmmmvG4FMAAIDxZNWXW2SfM36d7GGI2+OVDVu3yc7TK2I+dtaUMrGYTTrEMmd6ZSDA4g+z7DStXGzWlP41EQnidDoD1VTmzZsnZgPVJrxer6xdu1YmguHCLT6PR3qam0OCKf5qKx3r14e8N8PnE1tQRRW17V65UlY1NxsaG8GWIBkZemJoUFAlQojFlp/PBBIAAAAAAGloWmWx/O+Pjh5x9ZjhKsiEvB4WllELJ0ajgg9GqfOmAqMBHZ/PlxIBHXWd2+iiTE3bO/V1dqOy7baRh2IGAjHB71fj5toUgETr6uqSoqIivShZNOqeqdzc3FENz4x5+KUgcjU4VdHeH3ixRwjAjMb8Sp/PJx0bN+q2+c03B71uyc6WgurqQPglEHwZaLY8qpEBAABg4krpuxZVCcxf/OIXcumll+qLOqqpm+dUUEVVdDnkkENkyZIlsnDhQv3FTH35UuU11Re0zz77TJYvXy5vvvmmPsZfrcW/Vf2q/gEAAGIxt9pYCfDRsLqm3lC4Ra0m99XzN8mU8iI98YfxzeVyyfPPP69/R25paQmEWVRTIXK/xx9/XEpLS2Puv7i4WCaK+tpaWfP44xHDK/qxCqZEudlBTRLtabMFgixWFbwf80+Q3jILCnZUUhmiZZWWismS0l91AQAAAABAmlSPUQGOrh5XlDBMj8yeWm54bKkQDImncov6uQwV/BkrKihiNCAS759Bj7NXN1U9xgh1fV6N/7zvHCi/vOjbcY0FAPx23XVXeeqpp/QCZ2pBYBVGaWho0E09Vs0eR6Vy1U866c3M1K19BOGXQZVfRin84unpkZbVq3WLRM1zRKv6oirCmK3GKpYBAAAA6SDl7/j56U9/KjU1NTrQ4g+4qItkKrDyxhtv6DYU/wW14AtaF198sfzkJz8Z9bEDAIDUoCYgNze2yuoN9bKmpl6HQi743sGy57zpMfeVn5slUyqKZEtj9NWOxor6LN86ZHdDx06vKkn4eJCaTCaT3H///cO+T4VdCLdEp8rat69cKS//5S+Gjs/v6kr4mMYDs82mwyq5QVVVIoVXsisqxJplfCVUAAAAYDx7+OGHA4/VYmB77LFH3H1+8skn8umnnwb2TzvttLj7xA5Nr/022UMAEMO1NXVNWLWpCe77f75/qBy5ZMGw1WM6uh3i8/WlXOUWFfZJBaoiSrpWz/F6fbK9vVs8HuM3Tn/9wj/I5xvqBirC2PWW6jEAFBVgmTZtmm6JlG7hlnjCL1O2bJEFa9eO+Zgc27bp1rBixaDXMkwmHXCJWPVl5kxd2Z6/1wEAAJDOUj7cotx2220yadIkue6666S3tzfkl/ChVoPxh2H877PZbHLjjTfKlVdeOSbjBgAAY8vj8cr6Lc2yprahP8Siwiy1KtDSoFdPC7bHnKmGwi3KvOrKpIZbqkoLZP7MKl15BelPrZzlr6SiKqsEV1gJfnzhhRfKoYceGnP/VqtVCgoKpL29fcj3qXMYkZeXp8/hdrsl3WT4fLqSilqVy19RJXirnx94bEqBVSjTRkaGZJeVDRlW8bfMwkImWQAAAIA4nXHGGYHfq6+66qqEhFvUSs+33HJLYJ9wS2KVFeUlewgAUsBxB++WkOox0UIxwa87XO6Eh0NSpfKM0XCO0t6dGp9BhUyMqtvWJvXb2nWLp3pMtPBL//NRwjIDW1UtHsDEob575OfnS0dHh4x3qoKLUU6bTWxud8Lnl/p8PunYuFG3zW++Oeh1S3b24IovA8EXtbXl5iZ0PAAAAMCEDLf4J2SOPPJIufbaa+Uf//iHvoinDHUjlD/4olbUOfbYY+WGG26Q3XYb2UVCAACQuly9bvliU5Os3lCnq7Co8IoKsny5uUl63Z4R9aGOM0oFS177YI2MtupJJTJ/RpXMm1E1sK3Uj9WEEVKb+j20s7NzUFglUuvpGdnqgNu2bTM8HlVdZbTCLer3cdW/KmOfKswez46QissltvDgysBjq9stxCpGzpafHzGgEh5eUcEWkyVtvmoCAAAA4+Z7aKKD4/45FgLpAJD+1WPUdfOIYZjOHqksHbxSfTqFW+K5Xq5+Dmkf0Inzz8FfPUY1o7LtNtn40q+ktDD2G6bVfR/Bi5YCSH1nn322bmp+S80NqUouwVv/YzVPlu6ynE7Dx36w997itNvF7nKJ3eGQbKdTb1WfWQNbu9OZ8PCLp6dHWj7/XLdIssrKBodfBraqIozZak3oeAAAAIBYpdUdR7vvvru88MILUltbK88884y89957smLFCv3FyB928V/gq6iokMWLF8v+++8v3/ve96S6ujqpYwcAALHr6nHq4IquwuIPsdTUy4atzeLzxXehT/Vl1LzqKkkUtSra7CllOjCj+tXbGVUyZ3qF5GRlJuw8SAyPxyNtbW1DhlX8FVcSXclE9WmUCp/U1NSMSrhFKSoqGpNwizVCdZWQ8MrAcxavd9THMl6YrNZhwyr+Zs0mWAcAAACkKm4IBQAMxWa16KpRiawctdPUcnnkF2eNuILMUNVj0jUYkijpHtDpcfZKXrax+YxXl6+WYy65U/Jz7P2VYHIiVYjJGng+cgUZ1dR/4wDGVnZ2tsyYMUO3SLq7uyOGX/zbrq4uGa+VW3wZGeLKzNSV7lXARbW2CO/b+6OPpLgt0iujx9HcrFvDihWDXsswm3XAJVrVl+zycr57AgAAYNSl5Td8FVS5/PLLdfNT5S5V6j8vL0+XvwQAAOlDrQjWH16p1xVY1tT2bzc3Gr+Zf7Qrt8Qq02aRudWVOwIs6vGMKtlpWjmTLinA4XBEDKn4K6/491X1E//KtWMtnvCJCreMZv8lJSWGj83w+XQgxRYhtOJ/7H8t0atXjWdq5a1IAZXwAIu9qIiJCAAAAAAAAMSsoiRffvCNfRJSPaY/EOMYMiijWqRFr1TQwSh1rlRgNKDj8Xil2+GSZFPzH5k2Yyv9qz9XVT2mtaNHN6OyMq2Dwy+52QPPDQRnggMzA6EY/35udibXSYEEy8nJkZkzZ+oWiQq3hFd78W9VU5Vhku2I664TX12dtNfU9LcNG8TV3j7scSrY0mcyjVp4xmsySa/NpufREjl31uf1SkdtrW6b33xz0OuW7OyoVV9Us+XGXsELAAAACDdu7qRUgRZCLQAApL5tbV3yxL8+3BFmqamXpu1jX5a6saVDh2qKC3JiPlaFUqJREyDzZ/RXX+nfVsr8mZOkuqpEV2nB2FEhFBWAjhZW8VdfUfupcIF8OGq8yQi3qJ9jb2endDc0BFpP0GPVmlVpebs95DizxxO5ukpYcMXqdgtThiNjzc0dHFSpqhr0nAq2UDYeAAAAQDx6e3sDjzPVisMAACS5eoy6TtnV4xoUhqksyZ+wlVvSffyJrDyjKgM5XO1Sv234m84jueiEQ+SOq042dGxHl0PsmVYWMgNilJubK7Nnz9Yt4t/5XV0hYZfwAIxauG402Ww2OfCSSwYF35ytrdK2YUNI4MW/7di4Uby9veLIyhrR4m92l7GAYkdenqzYe+9AHyokk+V0in1gq/cdDv1aIufgPD090vL557pFouanogVfVEUY5q4AAAAwEny7BgAAY0pd5L/4N09IKlDhmv13H3zBdDilhbm6CktZUe6OSiwDYZbJ5YWs7jXGN9u89tprg8Iq/hCLx+OR8WK0K7dsXrNG3r/xxojhFc8wEwRF+fmym90eEmCxeL2GxzuRmCyWkEoqUSutVFSw4hUAAACAMVNXVxd4nJdn7CZkAAASSV13z8ux6zY1QX3++uLvyJbG1h0VY7ojVJRR+9079iNVj0lW5ZZUCbcU5Ezc6jnK1y78g3y4ujZq9ZiQ/SEqyFA9Bgj7Oz8vT7do4ZfOzs5BoZfgMIzT6YxrDBUVFRH/n7QXFUnlnnvqFs7n9UpXXZ289Nxz8uGLLw7Zv93pNBw88YdnVHUY9ThamEaHX1TYxR94CQq+qMdqTi+Rf+s4mpt1a1ixYvBYzGYdcAkEX4LDLzNnSnZZGX8HAgAAQCPcAgAAxtT0qhJ9gV+topVsa2obDIVb9LHP3JDw8SB2JpNJbrvtNn0Re7wbSbilz+cTR0tLSDBFtfq1a4c8Tl3c7tiwQd5/6ilDYyvs6BBRDQFZJSUhVVWihVfUJEjGCErTAwAAAMBYcbvdsnTpUn1jkfq+PWXKlGQPCQCAUbHPLjN0i7V6TEj4pSs8DKO2zqCwTNB+l0N6nDuqo/mpgIMR6typQAU3jFI/o7T/DAN/DvFWjzGZMnTQZchQTFAYJvz1koIcPWcCTATqu0p+fr5uO++8c8S/rzs6OkLCL8EBGNWGC79UVlbGPC6T2Sz5U6eKu6Bg2PdOnjZN9lqyJFD1RVWD6R3hXNtIKsMEwi/Z2bpFmx/0B17sEQIwajG7RMVN+rxe6ait1S0SS3Z21KovqrEAHAAAwMRBuAUAAAzJ6XLLF5saZfWGellTW9+/rWmQX1xwnHz70D1i7s9sNsmc6ZXy3y82S7Kt3rBjFVKMHnXx2F9VxV9hRVVVCa62ctZZZ8lBBx0Uc98Wi0UKCgqkra1NxjtVfr3mzTeld9u2QeGVQKWVxkZ9cThcZ26uVFZX64vQtoHKKrrCysBjq9ud0JWZxit1YT13ILAyVKWV7PJyMdtsyR4uAAAAAMTs888/l+uvv17f7OW/aWzXXXdN9rAAAEi56jFTKooM9dHr9khH946wi9pWlOSnd+WWOKqeqMo4af8ZuhJTfUZVBWrt6NFNpCXm41tev1WKC3JiPq7b4ZLt7d1Uj8G4ov47VvOHqs2ZMydi+EXNLQZXewkPwajKLUb5v08NZfZee8khl10WMiZna2t/2GUg8BKyra0Vn7t/8UiH3S6JoMIvPdnZukUNvwQHXsK2tgSGXzw9PdLy+ee6RZJVVha16osKFJks3AIJAAAwXqTtb3afffaZLF++XFasWCF1dXX6Bkl1w19ubq4UFRXJpEmTZPHixbLvvvvKLrvskuzhAgCQ8jq7nbK2tkFW16jwykCIpbZBNmxt1hfUw636couhcIsyf2ZVUsItFrNJZk8tl/kzqmTejEo5fPG8MR/DRHTdddfp393ivcgbjfrdbyKEW5RHjj5aXzCOVV5Xl+w2zJ/BRKUudmdXVAwOqEQIrbAqFAAAAIBkUBVLVRuJu+++W5544omYz9Hb26sXn3C5XHrfX7VF+eY3vxlzfwAAIDKb1SKlhbm6xWvR3Gny7v1XJKR6TLKCIakS0FHBjnT/DPk5xm52f+PDtXLcpXclpHqM2qr/xoFUp77vqPlF1ebOnTvodfVdSH1HMkoFZIYTXhlGjSmruFi3yj33HPR+n9crXXV1Ouiy4U9/UieR0abDLzk5ukVi8nr1vKU9SgAmkeEXR3OzbvUffDDotQyzWfKmTo1c9WXmTMkuKyO4BwAAkEbS6lulKgl5//33y7333iurV6+O+OUi+JfRBx54QG/nz58v559/vvzoRz8Se4LS6wAApKuWti4dWlFVS1QFFn+YZXNja0z9qOOMmlcdexnnWNgzrTJnesVAiKUqEGZRwRYuqg/N4XAEqqmo8LD/8ZQpU+Soo44y1Ke6MDwcdQ6jiouLpaamRtKdWv3IX0nFv9VVVsIqrWBk7MXFw4ZVcqqq9CRBhsmU7OECAAAAQFRqQYfa2toh3+MPorS3t+sWL/9cy6xZs+T444+Puz8AAJB4Kkiw/+6zYz7O7fFGCMOo7UBIpits3/96UIgmeFG0eIIhqq9UYDSg43S5xdXrkWRTFVcsFnPc4Zx4q8coWZnWyOGXaGGZgW1VaUFCQl9AIqjvQ5mZmYaPV/Orw4m1MozJbNYVSlTrHrgfLtl8ZrN05+ToNlT4RYddgoIvdqdTsh0OsbrdCQm/9Hm90lFbq1sk1pycHWGXsKovamuLMn4AAAAkR9rc3bl06VI599xzZf369YFJmvBJluBgS/B7Pv/8c/nxj38sf/zjH+W+++6Tgw8+eAxHDgDA2FP/DtZvaw+pwNK/rZem7Z0JOYcKxhilKrckQl6OXQdlVH/zqqv6tzOqpLqqRMxmblb38/l80tHRERJWCW/qefW6CrdEoqrhGQ23lJSUjHq4JZWZPZ5BoRV/UMX/WK1clKgLuOOZJStLB1IGhVTCAizZ5eViiWPSAQAAAABSUbSVZoPnQxK1Gq3qU91s9eSTT4rVak1InwAAIDVYLea4qseo3xO6Ha5A+CUny5b2VU+MBnTSPZyjqD/HRHK43Lo1tHTEdNxPTz5cfn/Z9w2dc11tQ6DqjPqzZKE7JNuDDz6o5z4bGxt1U5VcVPM/bmpqGlS5ZaS8Xq8+Ph0MF34xe72Rq74MPE7U3Km7u1u2ffaZbpFklZUNqvqi9vNnzNBhIpOFv1MAAADGUlr89nX33XfLxRdfrC+ShFdn8T8XSfj7vvzySzn88MPlzjvvlPPOO29Mxg4AwGiHFjbWb+8PsegKLKoSS39FltGeEFi3sVE8Hq+hlaBUACUWJQU5Orgyf8YkXYHFX5FlcnnhhC4h7Ha7dSAlUlgluKn3eDzxrRw2khWG4gmfxBNuGUl4JuH6+nQgJVBdJUpwRb1m8fnGfnxpRJUKV2GUSFVVwp+z5uZO6P/nAQAAAExs0eZCYn3PUNR3rp133llOOOEEPS9TVlYWV38AAGD8Ub8v5GbbdZtSMXzl9qE89v/OlubWzhFXkIlWPSZZ4ZCUCefkGq+ekyqfIZ6AzknX3C///WJzYN+eaZXC3CxDFWTUcaoSjomK74iD+u+ntLRUtwULFkQMqMQzZxvvvG+q8KrwS26ubtEWEDzsrbfEFOf33OE4mpt1q//gg4jzmPnTpoVWfAkKv2SXlTF3CQAAMNHCLQ888IBcdNFF+rH6ZVA1/+SM3W6XhQsX6i8ChYWFkqPS3t3d0tbWpqu1fPbZZ4HVx/2/SKqbgC+88EK90thZZ52VxE8GAIAxqiLLFbc9oyuxrK1t0KsfJUOv2yM1ddtkp2mxlUxWZk8tF4vZJB5v6E3/KqwSqMDir8gyo0rKivJkIlG/vwxVYcUfWGlvbx+zManzpWq4JZGVWzJ8vkFVVnR4Jfy53t5Rv5Ca7uxFRYFKKkNVWskqKdGl1AEAAAAAkZ1xxhlyyCGHRHxNzZccdthhgTmQk08+Wc4555yY+lfHZmZmSn5+vkydOlVyo9xYBAAAkGjTKot1i7d6TCAYExR+GS4o0+PsDfSnAg1GqPOlfeWWrtSoPqPCJka1d4f+OThV5RgD1WOCfz8eUSAmLECzx5yphhYFxMRjjmNeTM0Vq/CMuv9tvLN4PIbnY/1HxRs76fN6pb2mRjd5441Br1tzcgKBl/CqL+qxLUrVGgAAAKRpuKW2tlZ+8pOf6Mf+iRl1kWKPPfaQyy+/XL71rW9JVlb0L7hOp1Oee+45ufXWW+Xjjz8OCceoflUVl+nTp4/Z5wEAIBFy7DZ59J8rJBWogI2RcIsqef+j4w+Q/Bx7fyWWmZNkbnVlXBffU526wNjR0RExrOKvvuIPrqjfYVKNGqP6DEZWqkpEuMXd0yPdDQ2DWk9Dg3y1ZcuILn6qYIquthIhrKJfc7nE6vEkpLz1eGWx2wOBldyqqqjhleyKCrFkZiZ7uAAAAAAwLqh5jJHOZUybNk0OPvjgUR8TAABAulePcXu8OuiimtFF1lKl6klcwZDxENDpTGxAR91TpCsFxdhvx1u3SZ6BcMu2ti5ZU1NP9RiMyLx58+Qf//iHnlduaGjQrbGxMbBVrampaVyEX7LimDPfOG2arJ85U+wOh+4nS22DHzudel44Xu7ubtn22We6RZJdXh616kv+1KlisqT0rZsAAABJkdK/If3sZz+Tnp6eQCBFbW+++WYdbBlJST9V2UWtUnbSSSfpgMuVV14ZeE31e80118ijjz46yp8CAIDBOrv7L8Tk5dhjPjY/N0tXONna1CbJtqa2QY43eOw91/xAxgO32x0Ip0QLq/j34ykxnWxq7KpSTFFR0aiEW7q6uuTj++4T97ZtIcEV/+Pezs6ox3bm5UnprFk7gisRAizmcXABd7RkmEz6wqq/kspQlVZs+fmU1gYAAACAFOSveA8AAICRL8RWWpirm1GH7LmzbPnHrw1XkFHVZ5IfDEmNcIsKdhj9PTgVAjomkwpcGVv0651PvpTvXHFPxOoxRirI+F/PtFkT9OmQipVfysvLddt1110jzus2NzeHhF6CgzDbtm1Li/CLCqEY1ZOVJR6LRbry8nSLZFJdnSxcvTqOEY5gHE1NutV/8MGg1zLMZsmfNm1Q1Re1VeGX7LIy5mUBAMCElLLhlu7ubnnhhRdCgi233367XHjhhTH3pY697LLLJCcnRx/v7/Pvf/+7DrlkZxv7kgwAwHBa2rpkdU29Xm2nf9ugt1saW+XOq06WC084xFC/86qrUiLcoiq3THRXXXWVfPrppzIRqIDOcOEW9TuWq60tpLpK48aNI+r/5Z/8RLINrMCT39kpe/73vzEfN95lFhYOrqgSIbCSVVoqpjjKnwMAAAAAkuvNN98MqdwCAACAsWGzWmRyeZFuRqvHdEQMwwwdigkO0Xi9PsPBkNGoejLWAZ2uHpf4fH0pMX6jN6FHCucEV4/ZaHA61p5pHTL8Eh6WmTO9UnaeXmHsZEi58EtlZaVuu+2226DXPR5PSPgluOqLeqzCL6mwgEJuHAEcp334RUbVwolG+TIyxBTnz6hPLS5ZU6ObvPHGoNetOTlRq76ox7acnLjODwAAkKpSNtzyzjvviMPh0F/+VDvkkEMMBVuCnX/++fLMM8/IGwO/EKr+3377bTnqqKMSNGoAwESkLuzUb2vXQY/wEEtza/RqE+q9Rs2fWSX/XrFGxpKqMjN/RpXMm1E5sK2S3XaaIulErUCjqo+oSir+qiqqqQt7hx56qKE+jVQySVebVq+WrKamkOBKeFPVVrxhFwLVZb2Mww4Ts9erK6nYgqurhFVZwdDMmZlDBlZyq6r69ysqxDKCi7YAAAAAgPT21VdfycqVKwP7bW1tMmPGjKSOCQAAACOvHlNSmKub0TnKHmdvXOGOVKh6ohgN6KTK+OOrnjM6ASOny61bY0vHiN5/xalHyC0/+a6hc7350TrxeLxUj0kTFotFqqqqdIvE7Xbr8Et46MW/r+bZxyL8csxvfyuH7rNPIADStmGDdAxs1X7Hxo3ic7sjHuvIyhrVyjDLFy8WZ2amZDmduh+1tQ9s/fsWr1fi4e7ulm2ffaZbJNnl5YOCL/4wTN6UKWKypOxtoQAAAENK2d9iNm/erLf+qi3nnntuQvo955xzAuGW4PMAADCSYMTG+u07KrGoMEtt/7ajO/ZqE6ofo+ZVV8poUeXXVXhGVYfp31bq7aSywrQse/vvf/9bnnvuOR1iUaEWVYY53L777ms43FJcXCwTxQvnnSeT62P/71b9V3P40qViToPy1kmRkaEvPoaHViKFVzILCtLy/0MAAAAAwOj417/+JZdccklg/5ZbbknqeAAAADB21HxBTlZmXH28+8CVCakek6xwSKpUnlFVUIwaDwGdi295Qj7fUBd39Zgdr/dv87IzxWQyxfnJECur1SqTJk3SLZLe3t6IlV+Cwy+JoBaozCop0a1yr70Gve7zeqVr69ZA8EUHXmpqpHXDBnFkDv93owqgGOWw28VjtYrbZpOO/PyI77H29gaCLuFbFYSxxDl33tPUpFv98uWDXsswmyV/2rRA+CUQfBkIv2SVljLnDAAAUlbKhltUicNgixcvTki/++yzj976f0ELPw8AAKoE+PotzYMCLGtrG8ThirzyhxGquotRKmwSr8nlhYMCLKoaS1lRnqQCvdpUT48OpXR1dcm8efMM9aP6+OKLL4Z8TzwX2MZruCXD5wupqKK22T3GJwgmYrDFlp8/OKxSVTXoOXXxkJVzAAAAAABGqEot/hVz1bzH17/+9WQPCQAAABOwesygMExQ+GW4sEy3w6XDDxM1GKJ+BqlABUyMau92JKR6TDj1HSc/xx4aisnxh1+Cn+/fHn/wbmKzMuc22mw2m0yePFm3aOGXpqamkNBLcBUYNf8/EhUVFUO+bhoIcKg29eCDA8+3t7fLQ9/73qhVbnFbLDrYMuz7bLb+8EtBQcTXbb29OuSS7XCIPUIAJp759T6vN1DxJhJrTk7Uqi/51dViy8kxfG4AAIB4pexv9NnZoasalJaWJqTfkpKSkP2sEZQhBACMT+pC2rqNDTpkEqjGUlMvX25q0gGX0Va/rV1aO7qlKD/2CwMqhDLSC34zJpXo98+focIrKsQySeZWV8Z1kTXeCjjqgpK6aKWaCpb4q6r4H/v3nQOrpah/r1944QVD5ysqKhr2PSO9gDYewi0Wt1uHVWxBoZXAduCxes3q8eiKKwhlttlCKqlErbRSUSHWsN9nAQAAAABItMyw1WinTp2atLEAAABg4laPUW1y+fBzcpGoeVmL2Vh1jm5nrz7Wk4DqMUkLhoyLgM7oVNBR4Sn18xnpz8jx3h2GzrOpYbu89fEXESvIUD3GWPhlypQpukXicrkC4Zfg0It/Xy3ioP5uKSsrM3R+1cdI/PSrr8S5dWug6ku7f6sqwNTWis/jiVq1JRF6bTbdooZfXK7BVV/8lV/iDL+4u7tl26ef6hZJdnl51KoveVOmsHAjAAAYVSn7m8aMGTNC9lWFlby8+FeSD1+ZPfw8AIDxT60Ms/+PbpGaum3i8/WvbJksKliz326zYj5OVVcpLcyVbW1del9dtN1pWkVIBRYVZpkzvUKy7DYZC2oFFn9ARW2jBVfUYxVwiYXD4dDNSCg1PNgaiX9MRi5MpkS4pa9Pr+wSHlaJFGCZiBVUhpWRoaunqGBKblXVkOGVzMJCSjQDAAAAAFJG+Eq2Xu/oL9gCAAAAJLp6jFFH7DtfepffpavHRKwQ09kz4uoxSQuGdI1OMCRWKshhhMfjla6e+H5+iaAqttgzh6+mEcnyTzfIadc9OGT1mB2hF7sU5gZVkgkLw+ht0GPVjI5rPC/SoBZmiLY4g1oAU90nqEIyoxVuUQtkFlZViVRVSeVeew163ef1Sldw8CU4/NLcLGOhNzNTt/Yhwi8Rq74MPDYNVHk1oqepSbf65csHvaaCLXnTpkWs+qK2at6d+XQAADAuwy1LlizRN5iqFL6yYsWKhARRVD+K6lf1v99++8XdJwAgvahQyNbmtqQHWxRVLcZIuEX5zU++K3nZdh1mmT21PK4Lv0OWEu/pGbbKimqdnZ0ymtQ5opU2jjd84q8mM5IqL0bCM0aZvN4dQZUoVVbU1up2x3Vxaryy5eWFVlOJ0rLKysQ8gtLRAAAAAACkml122SVkf8uWLVJaWpq08QAAAADJrB4zqazQcEDDXyFkcBgmeijG/7qaf56olVs6up2SCkarek5w9RhV4cWITJulP+ySEykQkzXwfLYsXlAti3dhkWa73R616stIqCowsS4UEc5kNkv+tGm6ySGHhLz27LPPyvK775Zk84dfoslUYZfw4Iuq+hJn+EVVtNFBnw0bIr5uzc2NGnxRzZptLEgHAAAmjpQNt6jSgl/72tfk1Vdf1V9E77//fjnxxBPj7ve+++7TW9XnYYcdZriEIQAgOdTFo7rmNlm9oV6qJ5XoaiWxMptNuqLJyi+2SLKtrqk3fOwZxxoPaPrDHP4qK+FBleCmygKngtEMt/j7Hyrc4nG5pKexUbobGvpbfb3eNm/dGvOYLG73oJBKSJWVgccWj0dY0ySUyWodFE6JFFzJrqgQW05OsocLAAAAAMCo2n333fVNOf6bd15//XX9HAAAAICRs1jMUlKYq1syKo7kZmcmvfqJ0cot6R7OUVRAaTS5ej3S2NKh21B+duZRhsMtj/xjuThc7pDqMcEhmolUPea4446TvffeW39PVlVcwrdq0c7KykrD/Y+kMkwqcNnturUVRgj99fXpexLCgy+BAEwc4Rd3V5ds+/RT3SJR8/ghwZeg8EvelCm6MgwAAJjYUvq3gRtvvFH+/e9/6xuZ33jjDfnTn/4k55xzjuH+1PGqHxVsUVVbfvGLXyR0vACAxFHhi9q6Fh3+UNVN+rcN+rF/9ZcbzjtWrj3nGEP9z59RlRLhls2NrQntr7e3d9iwir/yivoZpxM1biNUueLc3Fzp6uoa8n1r/v1vcb733o7wSlDraWgQZ2vkPyt1SSfjsMOkLyNjcHWVoLBK8GvmNPvZjwVVnni4wEpOVZXYi4ooYwwAAAAAwAD1Hfnss8+W//f//p/ev+uuu+Tiiy/W10MAAAAApL5//vHHgeoxah48UClmmAoy7d3+5/qPUc3j9Y15OGS0gyEjVZhrvBpEqgR04vkMv7j/H/LFpsYhq8f0h16CKsYEV5AJC8OEv56fY9f32qVL5ZcZM2boFkl3d7e+r2I0K8OkvIyMHeGXSK/39Ynd5ZKdv/xSqhL8edWCoqrVL18+6DUVbMmbNi1q1Rd1TwH3CgAAMP6ldLhl8eLFctNNN8k111yj9y+44ALp6emRn/zkJzH3dfvtt8ull16qH6uwzA033CD77LNPwscMAIiN2+OVrzY39QdYNtTLmtoGvV23sUGvLDIU9T6j5s2okrE0ubxQB2rUefu3lfpxWVFeXP2+88478uKLLwZCK2qVkfFqpOEW9e+8Wg2ka6CyimrqMuDQ0RaR12+4QSbXx/7flLp0cvA774jN7abKSoSSw4MCKhHCK9nl5WK2TpzVggAAAAAASKQrr7xSHnzwQamvr5fa2lr56U9/qkMuAAAAANKrekxxQY5uRqg5UjW/PlQgpq0rbD8oSKPCDRO3ckv6fwYVdhquekzT9k7djFCBgrzszMHhl7CwzI9POkwybak975uTk6PbeK/cEpeMDHHa7WIa40U7fR6PtG/YoNum11+PeP9BtOCLatZs4wExAACQOlI63KJcffXVYrFY5Gc/+5l4vV4dUHnsscfkiiuukGOPPVYyMzOjHqtS1uqG39/+9reyYsUK/UVO9fXLX/5SLr/88jH9HAAw0TmcvbJuY2NIFRa1/XJTo+EVZFQQxigVMEk0dUFnxqQSmT+zSuZVV/VvZ1TJ3OrKiBei1L9r/kDK9OnTDZ1TVWD55JNPZCLY1twsHZs3ByqpdA/RPD2hKwR5Fi0SKS4Oec7idodUVlGldY3KdA8dxBpP1GopqlRwpKoqIeGVigqx5Y596XYAAAAAACaavLw8ef755+Xwww/X15nuvfdevRLtH//4RykoKEj28AAAAACMATVXnW236VZVOrbfA2ZPLQ+EZdTilsmgwhVGpUpAJ57PMNoVdNQ9d6qykGpDueSUrxnqf/WGOvnbG5/okFWqV49R9yGq6i0q5OLfBj92OFLjv6dEyDJ4D4XXZJL3liwRu8Oh+8gK3jocuiqMkYVD1SKn2z79VLdI1D0KgeBLcPhl5kzJmzJFTGazoc8DAADGVkqHW2688cbA4yOPPFL+8Y9/6F+WP/zwQznxxBMlKytLdt11V5k/f74UFRVJdna2ruyibvRdvXq1rFq1KvALozpOfZFT/aj3BPc9Etdee23CPx8AjEcdXQ4dOtlRiaV/W1PXov8uTiRV3cXr9YnZHPsFDFU5xSiL2SQ7TasIVGDxV2SZM71Csuy2kPe2tbXJW2+8pkMsLS0teqv+nVKP1Ws+n0//e/bCCy8YGktxWGBjPHv3ttuk/bzzDB07o7ZWpm7ZEgiyqFCLeYxXGUl1WSUlIdVUolVaySoulowUuGgIAAAAAAB22GuvvXSFXzV3sm7dOnnkkUfk5ZdfltNOO02OP/54WbRokQ7BAAAAAEAiHbLXHPnyuV/ox+p+AKfLrSvBGK0g0zlMeGJUKrd0jW4wZLQ/g/qZq8osyZaTlakrEBnxybrNcu09LySkesyO1we2uTuet2fGX1VGfbdWbfbs2YNeU/8PqEUnwkMvwfvOOBbdHGsqiGKEw24XR1aWbq0RXs/w+fTio0s++ECs3sQF4noaG3WrW7Ys4iKeedOmRa76MnOmvl9C/TcGAACSL6XDLddff33ILw3+x+oXQdVUSOWDDz7QLZLgm6j9x6qAjGqxItwCAKG2tXUNCrCoSixbm9rGbAzqAk1N3Ta9Ekys1DEqpDJU1Rh1YWPu9MqBSiwD2xlV+ljrCC/KtLe3y+233z7ke1QQUzUVconVRAm3mOK8oFG6fbtMRJbsbMkdqKgyZHClvFzMttBgFgAAAAAASA8zZ84MPHa5XIH5EbXIym233aabkpubqyu5qAr3sVDzK+vXr0/wqAEAAACMN+q7g1oMMiuO6jFqccuObkfU8Eu0sMxOBu4ZSLXKLUbDLaky/tGunjPS6jFDsVktQ4Zivr7PPDlyyYK4/h/Iz8/Xbaeddor8GTo6BoVegrepEn6xuN1i9RgLTalQy1D6TCbpzcwUSwKDLcPxeTzSvmGDbpFYc3OjVn0pqK4Wa3b2mI0VAICJLqXDLeEhFX9AJVJKNlKQZbj3DUcd76/4AgAIrc5S9rXLJBWoUI2RcIu6aKGOW1vboFf3mDmpWKaW5UpFvk0K7SK5Fo9Ib6e0bm+W7RvXyclnXi+zZs0atfCJutlg8uTJo9Z/qlIXRPyVVIKrqtiCn+vtFYvHY6gs7XiUYTZLTkXFsIEV1dQFGH6PAQAAAABgfKutrR00nxG8WJifWj1WtVhxbQEAAADAWDGbTVKUn6PbWJk1pUwHZfyhmXjCE/FQ4Yp0DrfEVT2nc2yq5/S6PdK0vVO3SLLtNsPhll8/9IquPDRcBRkVfFELT8yZM2dQH+o7vFpANbzyS3AYxr+oxWgrLSqSXc85R9pranTr2LhRB0RGwmm3D/seu8Nh+B6QuspKWa+qrTgcugJMttqqBWWdTv2cusck1r7dXV3SvGqVbpFkV1RErfqSN2WKmMzGqhYBAIA0DLcET7yMNJgSS4BlLPoBgPEmPzdLJpUVSl3z2FVpiUZVjznu4N0GPd/b26sDI6q1tLRIa2tr4LHaqv1pnlYpL2wXq7glo0XE2SKyUfpbuG3bthkKt6gVMa1Wq7jd7okTbunrCwRTbGGhleCtes3si145Z6KxFxeHVlOJEljR5XBNpmQPFwAAAAAApJhIC3/FG0xhngQAAADAePfgdWcMqh7T2eMcXCGms2fI6jH+ffWa2+Mds3DIWAVDhqMqoRiVKgEdFUAx6r7n3pGardtGtBBrSMWYoAoy4UGYwsq5stvsPeTggfeq5u11SnNz06DQi/+xulcmEWbuuqscccMNgX0VbOncurW/+slA4MX/uG3DBulpbBxx5RZFBVGM6s7Olp6BFonJ69WhF3/YJbAdeKzuVYn1aon6fKrVLVs2+HwWi+RPnx656suMGf33eLBwCAAA4yPccvrppyd7CAAwrqgLKF9tbtKBEFX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",
      "text/plain": [
       "<Figure size 3300x1380 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: E:\\불평등 연구\\데이터\\73_AI_성장\\Appendix_Figure_C2_ind_reg_full_tertile.png E:\\불평등 연구\\데이터\\73_AI_성장\\Appendix_Figure_C2_ind_reg_full_tertile.pdf\n",
      "\n",
      "Appendix Figure C3 predicted values (Growth Alignment, full tertiles)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Mean</th>\n",
       "      <th>CI 2.5%</th>\n",
       "      <th>CI 97.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>High</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>5.575</td>\n",
       "      <td>5.492</td>\n",
       "      <td>5.662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.087</td>\n",
       "      <td>3.992</td>\n",
       "      <td>4.183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>4.822</td>\n",
       "      <td>4.728</td>\n",
       "      <td>4.913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.098</td>\n",
       "      <td>4.004</td>\n",
       "      <td>4.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Middle</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>5.050</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Middle</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.090</td>\n",
       "      <td>4.004</td>\n",
       "      <td>4.175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Wealth            Frame   Mean  CI 2.5%  CI 97.5%\n",
       "5    High  Competitiveness  5.575    5.492     5.662\n",
       "4    High             Risk  4.087    3.992     4.183\n",
       "1     Low  Competitiveness  4.822    4.728     4.913\n",
       "0     Low             Risk  4.098    4.004     4.190\n",
       "3  Middle  Competitiveness  5.050    4.963     5.139\n",
       "2  Middle             Risk  4.090    4.004     4.175"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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",
      "text/plain": [
       "<Figure size 2220x1380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: E:\\불평등 연구\\데이터\\73_AI_성장\\Appendix_Figure_C3_alignment_full_tertile.png E:\\불평등 연구\\데이터\\73_AI_성장\\Appendix_Figure_C3_alignment_full_tertile.pdf\n"
     ]
    }
   ],
   "source": [
    "# ----------------------------\n",
    "BASE_DIR = r\"E:\\불평등 연구\\데이터\\73_AI_성장\"\n",
    "DATA_PATH = os.path.join(BASE_DIR, \"df_1.xlsx\")\n",
    "\n",
    "# Output names\n",
    "C1_PNG = os.path.join(BASE_DIR, \"Appendix_Figure_C1_policy_priority_full_tertile.png\")\n",
    "C1_PDF = os.path.join(BASE_DIR, \"Appendix_Figure_C1_policy_priority_full_tertile.pdf\")\n",
    "\n",
    "C2_PNG = os.path.join(BASE_DIR, \"Appendix_Figure_C2_ind_reg_full_tertile.png\")\n",
    "C2_PDF = os.path.join(BASE_DIR, \"Appendix_Figure_C2_ind_reg_full_tertile.pdf\")\n",
    "\n",
    "C3_PNG = os.path.join(BASE_DIR, \"Appendix_Figure_C3_alignment_full_tertile.png\")\n",
    "C3_PDF = os.path.join(BASE_DIR, \"Appendix_Figure_C3_alignment_full_tertile.pdf\")\n",
    "\n",
    "# ----------------------------\n",
    "# Load\n",
    "# ----------------------------\n",
    "df = pd.read_excel(DATA_PATH)\n",
    "\n",
    "# ✅ Appendix C (full-tertile figures) should match analytic sample\n",
    "if \"attention_check_pass\" in df.columns:\n",
    "    df = df[df[\"attention_check_pass\"] == 1].copy()\n",
    "\n",
    "# Ensure category ordering (important for stable plots)\n",
    "frame_order = [\"Risk\", \"Competitiveness\"]\n",
    "wealth_order = [\"Low\", \"Middle\", \"High\"]\n",
    "\n",
    "df[\"frame\"] = pd.Categorical(df[\"frame\"].astype(str), categories=frame_order, ordered=True)\n",
    "df[\"wealth_tertile\"] = pd.Categorical(df[\"wealth_tertile\"].astype(str), categories=wealth_order, ordered=True)\n",
    "\n",
    "# ----------------------------\n",
    "# Common settings\n",
    "# ----------------------------\n",
    "rng = np.random.default_rng(20260228)\n",
    "B = 2000  # draws for CI\n",
    "\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11,\n",
    "    \"axes.labelsize\": 11,\n",
    "    \"xtick.labelsize\": 11,\n",
    "    \"ytick.labelsize\": 11,\n",
    "    \"legend.fontsize\": 11,\n",
    "})\n",
    "\n",
    "# Colors/markers/linestyles (3 lines)\n",
    "colors = {\"Low\": \"#8B0000\", \"Middle\": \"#444444\", \"High\": \"#003366\"}  # dark red, gray, dark blue\n",
    "linestyles = {\"Low\": \"-\", \"Middle\": \"-.\", \"High\": \"--\"}\n",
    "markers = {\"Low\": \"o\", \"Middle\": \"D\", \"High\": \"s\"}\n",
    "\n",
    "# Base formula template (same as main models)\n",
    "controls = \"\"\"\n",
    "+ C(context)\n",
    "+ age\n",
    "+ C(gender)\n",
    "+ C(region)\n",
    "+ C(education)\n",
    "+ C(income_category)\n",
    "+ C(employment_status)\n",
    "+ C(occupation_group)\n",
    "+ C(union_membership)\n",
    "+ C(marital_status)\n",
    "+ political_ideology_0_10\n",
    "\"\"\"\n",
    "\n",
    "def fit_model(outcome_var: str):\n",
    "    formula = f\"\"\"\n",
    "    {outcome_var}\n",
    "    ~ C(frame) * C(wealth_tertile)\n",
    "    {controls}\n",
    "    \"\"\"\n",
    "    return smf.ols(formula=formula, data=df).fit(cov_type=\"HC3\")\n",
    "\n",
    "def marginal_standardized_predictions(model):\n",
    "    \"\"\"\n",
    "    Returns DataFrame with Mean/CI by frame x wealth_tertile.\n",
    "    Uses coefficient simulation with robust covariance (HC3).\n",
    "    \"\"\"\n",
    "    beta_hat = model.params.to_numpy()\n",
    "    V = model.cov_params().to_numpy()\n",
    "    beta_draws = rng.multivariate_normal(mean=beta_hat, cov=V, size=B)\n",
    "\n",
    "    design_info = model.model.data.design_info\n",
    "    rows = []\n",
    "\n",
    "    for w in wealth_order:\n",
    "        sub = df[df[\"wealth_tertile\"] == w].copy()\n",
    "        for f in frame_order:\n",
    "            cf = sub.copy()\n",
    "            cf[\"frame\"] = pd.Categorical([f]*len(cf), categories=frame_order, ordered=True)\n",
    "            X_cf = patsy.dmatrix(design_info, cf, return_type=\"dataframe\").to_numpy()\n",
    "\n",
    "            mean_point = float((X_cf @ beta_hat).mean())\n",
    "            sim_means = (X_cf @ beta_draws.T).mean(axis=0)\n",
    "            lo, hi = np.quantile(sim_means, [0.025, 0.975])\n",
    "\n",
    "            rows.append({\"Wealth\": w, \"Frame\": f, \"Mean\": mean_point, \"CI 2.5%\": float(lo), \"CI 97.5%\": float(hi)})\n",
    "\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "def plot_three_lines(ax, pred_df, ylabel, title=None, pad=12, annotate=True):\n",
    "    x = np.arange(len(frame_order))\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels([\"Risk frame\", \"Competitiveness frame\"])\n",
    "    ax.set_ylabel(ylabel)\n",
    "    ax.set_ylim(1, 7)\n",
    "    ax.set_yticks(np.arange(1, 8, 1))\n",
    "\n",
    "    if title:\n",
    "        ax.set_title(title, pad=pad)\n",
    "\n",
    "    # plot each wealth line\n",
    "    for w in wealth_order:\n",
    "        sub = pred_df[pred_df[\"Wealth\"] == w].set_index(\"Frame\").loc[frame_order]\n",
    "        y = sub[\"Mean\"].values\n",
    "        yerr = np.vstack([y - sub[\"CI 2.5%\"].values, sub[\"CI 97.5%\"].values - y])\n",
    "\n",
    "        ax.errorbar(\n",
    "            x, y, yerr=yerr,\n",
    "            capsize=4, elinewidth=1.2, linewidth=2.2,\n",
    "            linestyle=linestyles[w], marker=markers[w], markersize=6.0,\n",
    "            color=colors[w], label=f\"Wealth: {w}\"\n",
    "        )\n",
    "\n",
    "        # Optional annotation (dynamic to reduce overlap)\n",
    "        if annotate:\n",
    "            for xi, yi in zip(x, y):\n",
    "                # place text slightly above for Low/Middle, slightly below for High\n",
    "                if w == \"High\":\n",
    "                    dy, va = -0.22, \"top\"\n",
    "                else:\n",
    "                    dy, va = 0.22, \"bottom\"\n",
    "                ax.text(xi, yi + dy, f\"{yi:.2f}\", color=colors[w], fontsize=9,\n",
    "                        ha=\"center\", va=va)\n",
    "\n",
    "    # 4-side border\n",
    "    for spine in [\"top\", \"right\", \"left\", \"bottom\"]:\n",
    "        ax.spines[spine].set_visible(True)\n",
    "        ax.spines[spine].set_linewidth(1.0)\n",
    "\n",
    "    ax.grid(False)\n",
    "    ax.legend(loc=\"upper left\", frameon=False)\n",
    "\n",
    "# ============================================================\n",
    "# Appendix Figure C1: Policy Priority (full tertiles)\n",
    "# ============================================================\n",
    "m1 = fit_model(\"policy_priority\")\n",
    "pred1 = marginal_standardized_predictions(m1)\n",
    "\n",
    "print(\"\\nAppendix Figure C1 predicted values (Policy Priority, full tertiles)\")\n",
    "display(pred1.round(3).sort_values([\"Wealth\",\"Frame\"]))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.4, 4.6), dpi=300)\n",
    "plot_three_lines(ax, pred1, \"Policy Priority (1=Industrial support, 7=Regulation)\",\n",
    "                 title=\" \", pad=12,\n",
    "                 annotate=False)  # annotations OFF for readability with 3 lines\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(left=0.13)\n",
    "fig.savefig(C1_PNG, bbox_inches=\"tight\")\n",
    "fig.savefig(C1_PDF, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "print(\"Saved:\", C1_PNG, C1_PDF)\n",
    "\n",
    "# ============================================================\n",
    "# Appendix Figure C2: Industrial & Regulation (full tertiles) – 2-panel\n",
    "# ============================================================\n",
    "m2a = fit_model(\"industrial_support_index\")\n",
    "pred2a = marginal_standardized_predictions(m2a)\n",
    "\n",
    "m2b = fit_model(\"regulation_support_index\")\n",
    "pred2b = marginal_standardized_predictions(m2b)\n",
    "\n",
    "print(\"\\nAppendix Figure C2 predicted values (Industrial Support, full tertiles)\")\n",
    "display(pred2a.round(3).sort_values([\"Wealth\",\"Frame\"]))\n",
    "print(\"\\nAppendix Figure C2 predicted values (Regulation Support, full tertiles)\")\n",
    "display(pred2b.round(3).sort_values([\"Wealth\",\"Frame\"]))\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(11, 4.6), dpi=300, sharey=True)\n",
    "\n",
    "plot_three_lines(axes[0], pred2a, \"Industrial Policy Support (1–7)\",\n",
    "                 title=\"(a) Industrial Support\", pad=12, annotate=False)\n",
    "plot_three_lines(axes[1], pred2b, \"Regulation Support (1–7)\",\n",
    "                 title=\"(b) Regulation Support\", pad=12, annotate=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=0.22)\n",
    "fig.savefig(C2_PNG, bbox_inches=\"tight\")\n",
    "fig.savefig(C2_PDF, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "print(\"Saved:\", C2_PNG, C2_PDF)\n",
    "\n",
    "# ============================================================\n",
    "# Appendix Figure C3: Growth Alignment (full tertiles)\n",
    "# ============================================================\n",
    "m3 = fit_model(\"growth_alignment_index\")\n",
    "pred3 = marginal_standardized_predictions(m3)\n",
    "\n",
    "print(\"\\nAppendix Figure C3 predicted values (Growth Alignment, full tertiles)\")\n",
    "display(pred3.round(3).sort_values([\"Wealth\",\"Frame\"]))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.4, 4.6), dpi=300)\n",
    "plot_three_lines(ax, pred3, \"Growth Coalition Alignment (1–7)\",\n",
    "                 title=\" \", pad=12,\n",
    "                 annotate=False)\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(left=0.13)\n",
    "fig.savefig(C3_PNG, bbox_inches=\"tight\")\n",
    "fig.savefig(C3_PDF, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "print(\"Saved:\", C3_PNG, C3_PDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04926202-1ddd-4444-aec8-783bc563556f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1a8125bc-6f42-4e4b-8f1d-c9fec264494d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: E:\\불평등 연구\\데이터\\73_AI_성장\\appendix_tables_B1_B4_full_results.xlsx\n",
      "Sheets: ['META', 'B1_policy_priority', 'B2_industrial_support', 'B3_regulation_support', 'B4_growth_alignment']\n"
     ]
    }
   ],
   "source": [
    "BASE_DIR = r\"E:\\불평등 연구\\데이터\\73_AI_성장\"\n",
    "DATA_PATH = os.path.join(BASE_DIR, \"df_1.xlsx\")\n",
    "OUT_XLSX  = os.path.join(BASE_DIR, \"appendix_tables_B1_B4_full_results.xlsx\")\n",
    "\n",
    "# ----------------------------\n",
    "# Load\n",
    "# ----------------------------\n",
    "df = pd.read_excel(DATA_PATH)\n",
    "\n",
    "# ✅ Use analytic sample (consistent with main results figures)\n",
    "if \"attention_check_pass\" in df.columns:\n",
    "    df = df[df[\"attention_check_pass\"] == 1].copy()\n",
    "\n",
    "# ----------------------------\n",
    "# Factor ordering (critical)\n",
    "# ----------------------------\n",
    "# frame: Risk reference\n",
    "df[\"frame\"] = pd.Categorical(df[\"frame\"].astype(str), categories=[\"Risk\", \"Competitiveness\"], ordered=True)\n",
    "\n",
    "# wealth tertile: Middle reference (set Middle first)\n",
    "df[\"wealth_tertile\"] = pd.Categorical(df[\"wealth_tertile\"].astype(str),\n",
    "                                      categories=[\"Middle\", \"Low\", \"High\"], ordered=True)\n",
    "\n",
    "# ----------------------------\n",
    "# Model specification (same as figures)\n",
    "# ----------------------------\n",
    "controls = \"\"\"\n",
    "+ C(context)\n",
    "+ age\n",
    "+ C(gender)\n",
    "+ C(region)\n",
    "+ C(education)\n",
    "+ C(income_category)\n",
    "+ C(employment_status)\n",
    "+ C(occupation_group)\n",
    "+ C(union_membership)\n",
    "+ C(marital_status)\n",
    "+ political_ideology_0_10\n",
    "\"\"\"\n",
    "\n",
    "outcomes = [\n",
    "    (\"B1_policy_priority\", \"policy_priority\"),\n",
    "    (\"B2_industrial_support\", \"industrial_support_index\"),\n",
    "    (\"B3_regulation_support\", \"regulation_support_index\"),\n",
    "    (\"B4_growth_alignment\", \"growth_alignment_index\"),\n",
    "]\n",
    "\n",
    "def fit_full_model(yvar: str):\n",
    "    formula = f\"\"\"\n",
    "    {yvar}\n",
    "    ~ C(frame) * C(wealth_tertile)\n",
    "    {controls}\n",
    "    \"\"\"\n",
    "    return smf.ols(formula=formula, data=df).fit(cov_type=\"HC3\")\n",
    "\n",
    "def tidy_summary(model):\n",
    "    \"\"\"Return tidy dataframe with coef, HC3 SE, CI, p.\"\"\"\n",
    "    params = model.params\n",
    "    se = model.bse\n",
    "    p = model.pvalues\n",
    "\n",
    "    out = pd.DataFrame({\n",
    "        \"term\": params.index,\n",
    "        \"coef\": params.values,\n",
    "        \"se_HC3\": se.values,\n",
    "        \"p_value\": p.values,\n",
    "    })\n",
    "    out[\"ci_2.5\"] = out[\"coef\"] - 1.96 * out[\"se_HC3\"]\n",
    "    out[\"ci_97.5\"] = out[\"coef\"] + 1.96 * out[\"se_HC3\"]\n",
    "\n",
    "    # Optional: significance stars (common in appendices)\n",
    "    def stars(pv):\n",
    "        if pv < 0.01: return \"***\"\n",
    "        if pv < 0.05: return \"**\"\n",
    "        if pv < 0.10: return \"*\"\n",
    "        return \"\"\n",
    "    out[\"sig\"] = out[\"p_value\"].apply(stars)\n",
    "\n",
    "    # Make a display column \"coef (se)\"\n",
    "    out[\"coef_se\"] = out.apply(lambda r: f\"{r['coef']:.3f}{r['sig']} ({r['se_HC3']:.3f})\", axis=1)\n",
    "    out[\"ci\"] = out.apply(lambda r: f\"[{r['ci_2.5']:.3f}, {r['ci_97.5']:.3f}]\", axis=1)\n",
    "\n",
    "    return out\n",
    "\n",
    "# ----------------------------\n",
    "# Fit models + export\n",
    "# ----------------------------\n",
    "meta_rows = []\n",
    "tables = {}\n",
    "\n",
    "for sheet_name, yvar in outcomes:\n",
    "    m = fit_full_model(yvar)\n",
    "    t = tidy_summary(m)\n",
    "\n",
    "    # store\n",
    "    tables[sheet_name] = t\n",
    "\n",
    "    meta_rows.append({\n",
    "        \"Table\": sheet_name,\n",
    "        \"Outcome\": yvar,\n",
    "        \"N\": int(m.nobs),\n",
    "        \"R2\": float(m.rsquared),\n",
    "        \"Adj_R2\": float(m.rsquared_adj),\n",
    "        \"Covariance\": \"HC3\",\n",
    "    })\n",
    "\n",
    "meta = pd.DataFrame(meta_rows)\n",
    "\n",
    "# ----------------------------\n",
    "# Write Excel (multi-sheet)\n",
    "# ----------------------------\n",
    "with pd.ExcelWriter(OUT_XLSX, engine=\"openpyxl\") as writer:\n",
    "    meta.to_excel(writer, sheet_name=\"META\", index=False)\n",
    "\n",
    "    for sheet_name, t in tables.items():\n",
    "        # A cleaner appendix-style sheet:\n",
    "        # keep term, coef, se, p, CI, and a combined display\n",
    "        out_sheet = t[[\"term\", \"coef\", \"se_HC3\", \"p_value\", \"ci_2.5\", \"ci_97.5\", \"coef_se\", \"ci\"]].copy()\n",
    "\n",
    "        # round numeric columns\n",
    "        for c in [\"coef\", \"se_HC3\", \"p_value\", \"ci_2.5\", \"ci_97.5\"]:\n",
    "            out_sheet[c] = out_sheet[c].astype(float).round(4)\n",
    "\n",
    "        out_sheet.to_excel(writer, sheet_name=sheet_name, index=False)\n",
    "\n",
    "print(\"Saved:\", OUT_XLSX)\n",
    "print(\"Sheets:\", [\"META\"] + list(tables.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b350358-67a8-4637-b91e-830a4eaf12aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "726d97dd-52d4-4cd0-a6c1-ac38eb4a74fb",
   "metadata": {},
   "source": [
    "# Mechanism. Figure D1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "97b2b21f-2a67-44b7-ad6b-26465ad8c057",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicted values (Low vs High): mech_econ_improve\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Mean</th>\n",
       "      <th>CI 2.5%</th>\n",
       "      <th>CI 97.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>5.441</td>\n",
       "      <td>5.365</td>\n",
       "      <td>5.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.462</td>\n",
       "      <td>4.386</td>\n",
       "      <td>4.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>5.212</td>\n",
       "      <td>5.130</td>\n",
       "      <td>5.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.497</td>\n",
       "      <td>4.417</td>\n",
       "      <td>4.578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Wealth            Frame   Mean  CI 2.5%  CI 97.5%\n",
       "3   High  Competitiveness  5.441    5.365     5.520\n",
       "2   High             Risk  4.462    4.386     4.545\n",
       "1    Low  Competitiveness  5.212    5.130     5.298\n",
       "0    Low             Risk  4.497    4.417     4.578"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicted values (Low vs High): mech_competitiveness_link\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Wealth</th>\n",
       "      <th>Frame</th>\n",
       "      <th>Mean</th>\n",
       "      <th>CI 2.5%</th>\n",
       "      <th>CI 97.5%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>5.468</td>\n",
       "      <td>5.390</td>\n",
       "      <td>5.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.414</td>\n",
       "      <td>4.330</td>\n",
       "      <td>4.496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>Competitiveness</td>\n",
       "      <td>5.191</td>\n",
       "      <td>5.108</td>\n",
       "      <td>5.278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>Risk</td>\n",
       "      <td>4.480</td>\n",
       "      <td>4.399</td>\n",
       "      <td>4.564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Wealth            Frame   Mean  CI 2.5%  CI 97.5%\n",
       "3   High  Competitiveness  5.468    5.390     5.550\n",
       "2   High             Risk  4.414    4.330     4.496\n",
       "1    Low  Competitiveness  5.191    5.108     5.278\n",
       "0    Low             Risk  4.480    4.399     4.564"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3450x2760 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved:\n",
      "E:\\불평등 연구\\데이터\\73_AI_성장\\Appendix_Figure_C4_mechanism_pred_coeff.png\n",
      "E:\\불평등 연구\\데이터\\73_AI_성장\\Appendix_Figure_C4_mechanism_pred_coeff.pdf\n"
     ]
    }
   ],
   "source": [
    "BASE_DIR  = r\"E:\\\"\n",
    "DATA_PATH = os.path.join(BASE_DIR, \"df_1.xlsx\")\n",
    "\n",
    "OUT_PNG = os.path.join(BASE_DIR, \"Appendix_Figure_C4_mechanism_pred_coeff.png\")\n",
    "OUT_PDF = os.path.join(BASE_DIR, \"Appendix_Figure_C4_mechanism_pred_coeff.pdf\")\n",
    "\n",
    "# ----------------------------\n",
    "# Load\n",
    "# ----------------------------\n",
    "df0 = pd.read_excel(DATA_PATH)\n",
    "\n",
    "# analytic sample\n",
    "if \"attention_check_pass\" in df0.columns:\n",
    "    df0 = df0[df0[\"attention_check_pass\"] == 1].copy()\n",
    "\n",
    "frame_order = [\"Risk\", \"Competitiveness\"]\n",
    "\n",
    "# Common controls\n",
    "controls = \"\"\"\n",
    "+ C(context)\n",
    "+ age\n",
    "+ C(gender)\n",
    "+ C(region)\n",
    "+ C(education)\n",
    "+ C(income_category)\n",
    "+ C(employment_status)\n",
    "+ C(occupation_group)\n",
    "+ C(union_membership)\n",
    "+ C(marital_status)\n",
    "+ political_ideology_0_10\n",
    "\"\"\"\n",
    "\n",
    "rng = np.random.default_rng(20260228)\n",
    "B = 2000\n",
    "\n",
    "# ----------------------------\n",
    "# Predicted values (Low vs High only)\n",
    "# ----------------------------\n",
    "def predicted_low_high(outcome_var: str) -> pd.DataFrame:\n",
    "    df = df0.copy()\n",
    "    df = df[df[\"wealth_tertile\"].isin([\"Low\", \"High\"])].copy()\n",
    "    df[\"wealth_LH\"] = df[\"wealth_tertile\"].astype(str)\n",
    "\n",
    "    df[\"frame\"] = pd.Categorical(df[\"frame\"].astype(str), categories=frame_order, ordered=True)\n",
    "    df[\"wealth_LH\"] = pd.Categorical(df[\"wealth_LH\"], categories=[\"Low\", \"High\"], ordered=True)\n",
    "\n",
    "    formula = f\"\"\"\n",
    "    {outcome_var}\n",
    "    ~ C(frame) * C(wealth_LH)\n",
    "    {controls}\n",
    "    \"\"\"\n",
    "    m = smf.ols(formula=formula, data=df).fit(cov_type=\"HC3\")\n",
    "\n",
    "    beta_hat = m.params.to_numpy()\n",
    "    V = m.cov_params().to_numpy()\n",
    "    beta_draws = rng.multivariate_normal(mean=beta_hat, cov=V, size=B)\n",
    "    design_info = m.model.data.design_info\n",
    "\n",
    "    rows = []\n",
    "    for w in [\"Low\", \"High\"]:\n",
    "        sub = df[df[\"wealth_LH\"] == w].copy()\n",
    "        for f in frame_order:\n",
    "            cf = sub.copy()\n",
    "            cf[\"frame\"] = pd.Categorical([f]*len(cf), categories=frame_order, ordered=True)\n",
    "            X_cf = patsy.dmatrix(design_info, cf, return_type=\"dataframe\").to_numpy()\n",
    "\n",
    "            mean_point = float((X_cf @ beta_hat).mean())\n",
    "            sim_means = (X_cf @ beta_draws.T).mean(axis=0)\n",
    "            lo, hi = np.quantile(sim_means, [0.025, 0.975])\n",
    "\n",
    "            rows.append({\"Wealth\": w, \"Frame\": f, \"Mean\": mean_point, \"CI 2.5%\": float(lo), \"CI 97.5%\": float(hi)})\n",
    "\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "# ----------------------------\n",
    "# Coefficients (Low/Middle/High with Middle reference)\n",
    "# ----------------------------\n",
    "def coef_midref(outcome_var: str):\n",
    "    df = df0.copy()\n",
    "    df[\"frame\"] = pd.Categorical(df[\"frame\"].astype(str), categories=[\"Risk\",\"Competitiveness\"], ordered=True)\n",
    "    df[\"wealth_tertile\"] = pd.Categorical(df[\"wealth_tertile\"].astype(str),\n",
    "                                          categories=[\"Middle\",\"Low\",\"High\"], ordered=True)\n",
    "\n",
    "    formula = f\"\"\"\n",
    "    {outcome_var}\n",
    "    ~ C(frame) * C(wealth_tertile)\n",
    "    {controls}\n",
    "    \"\"\"\n",
    "    m = smf.ols(formula=formula, data=df).fit(cov_type=\"HC3\")\n",
    "\n",
    "    term_low  = \"C(frame)[T.Competitiveness]:C(wealth_tertile)[T.Low]\"\n",
    "    term_high = \"C(frame)[T.Competitiveness]:C(wealth_tertile)[T.High]\"\n",
    "\n",
    "    def get(term):\n",
    "        b  = float(m.params[term])\n",
    "        se = float(m.bse[term])\n",
    "        lo = b - 1.96*se\n",
    "        hi = b + 1.96*se\n",
    "        return b, lo, hi\n",
    "\n",
    "    low = get(term_low)\n",
    "    high = get(term_high)\n",
    "    return {\"low\": low, \"high\": high}\n",
    "\n",
    "# ----------------------------\n",
    "# Compute objects\n",
    "# ----------------------------\n",
    "pred_econ = predicted_low_high(\"mech_econ_improve\")\n",
    "pred_comp = predicted_low_high(\"mech_competitiveness_link\")\n",
    "\n",
    "coef_econ = coef_midref(\"mech_econ_improve\")\n",
    "coef_comp = coef_midref(\"mech_competitiveness_link\")\n",
    "\n",
    "print(\"\\nPredicted values (Low vs High): mech_econ_improve\")\n",
    "display(pred_econ.round(3).sort_values([\"Wealth\",\"Frame\"]))\n",
    "print(\"\\nPredicted values (Low vs High): mech_competitiveness_link\")\n",
    "display(pred_comp.round(3).sort_values([\"Wealth\",\"Frame\"]))\n",
    "\n",
    "# ----------------------------\n",
    "# Plot styles\n",
    "# ----------------------------\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 11,\n",
    "    \"axes.labelsize\": 13,\n",
    "    \"xtick.labelsize\": 11,\n",
    "    \"ytick.labelsize\": 11,\n",
    "    \"legend.fontsize\": 11,\n",
    "})\n",
    "\n",
    "# Predicted plot styles\n",
    "colors = {\"Low\": \"#8B0000\", \"High\": \"#003366\"}\n",
    "linestyles = {\"Low\": \"-\", \"High\": \"--\"}\n",
    "markers = {\"Low\": \"o\", \"High\": \"s\"}\n",
    "\n",
    "def annotate_two_series(ax, x_positions, y_low, y_high):\n",
    "    for xi, yl, yh in zip(x_positions, y_low, y_high):\n",
    "        if yl >= yh:\n",
    "            ax.text(xi, yl + 0.22, f\"{yl:.2f}\", color=colors[\"Low\"], fontsize=10,\n",
    "                    ha=\"center\", va=\"bottom\")\n",
    "            ax.text(xi, yh - 0.22, f\"{yh:.2f}\", color=colors[\"High\"], fontsize=10,\n",
    "                    ha=\"center\", va=\"top\")\n",
    "        else:\n",
    "            ax.text(xi, yh + 0.22, f\"{yh:.2f}\", color=colors[\"High\"], fontsize=10,\n",
    "                    ha=\"center\", va=\"bottom\")\n",
    "            ax.text(xi, yl - 0.22, f\"{yl:.2f}\", color=colors[\"Low\"], fontsize=10,\n",
    "                    ha=\"center\", va=\"top\")\n",
    "\n",
    "def plot_pred_panel(ax, pred_df, title):\n",
    "    x = np.arange(len(frame_order))\n",
    "    low = pred_df[pred_df[\"Wealth\"] == \"Low\"].set_index(\"Frame\").loc[frame_order]\n",
    "    high = pred_df[pred_df[\"Wealth\"] == \"High\"].set_index(\"Frame\").loc[frame_order]\n",
    "\n",
    "    y_low = low[\"Mean\"].values\n",
    "    y_high = high[\"Mean\"].values\n",
    "\n",
    "    yerr_low = np.vstack([y_low - low[\"CI 2.5%\"].values, low[\"CI 97.5%\"].values - y_low])\n",
    "    yerr_high = np.vstack([y_high - high[\"CI 2.5%\"].values, high[\"CI 97.5%\"].values - y_high])\n",
    "\n",
    "    ax.errorbar(x, y_low, yerr=yerr_low, capsize=4, elinewidth=1.2, linewidth=2.2,\n",
    "                linestyle=linestyles[\"Low\"], marker=markers[\"Low\"], markersize=6.5,\n",
    "                color=colors[\"Low\"], label=\"Wealth: Low\")\n",
    "    ax.errorbar(x, y_high, yerr=yerr_high, capsize=4, elinewidth=1.2, linewidth=2.2,\n",
    "                linestyle=linestyles[\"High\"], marker=markers[\"High\"], markersize=6.5,\n",
    "                color=colors[\"High\"], label=\"Wealth: High\")\n",
    "\n",
    "    annotate_two_series(ax, x, y_low, y_high)\n",
    "\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels([\"Risk frame\", \"Competitiveness frame\"])\n",
    "    ax.set_ylim(1, 7)\n",
    "    ax.set_yticks(np.arange(1, 8, 1))\n",
    "    ax.set_title(title, pad=12)\n",
    "    ax.legend(loc=\"upper left\", frameon=False)\n",
    "\n",
    "    for spine in [\"top\", \"right\", \"left\", \"bottom\"]:\n",
    "        ax.spines[spine].set_visible(True)\n",
    "        ax.spines[spine].set_linewidth(1.0)\n",
    "    ax.grid(False)\n",
    "\n",
    "# Coefficient plot styles (Figure 4 style)\n",
    "marker_color = \"#FF0000\"  # red\n",
    "ci_color = \"#8B0000\"      # dark red\n",
    "\n",
    "def plot_coef_panel(ax, coef_dict, title):\n",
    "    # coef_dict: {\"low\":(b,lo,hi), \"high\":(b,lo,hi)}\n",
    "    ax.axvline(0, linewidth=1.2, color=\"black\")\n",
    "\n",
    "    y_low = 0.18\n",
    "    y_high = -0.18\n",
    "\n",
    "    # Low vs Mid\n",
    "    b, lo, hi = coef_dict[\"low\"]\n",
    "    ax.errorbar(b, y_low, xerr=[[b-lo],[hi-b]], fmt=\"o\", color=marker_color, ecolor=ci_color,\n",
    "                elinewidth=2.0, capsize=4, markersize=7)\n",
    "    ax.text(b, y_low + 0.06, f\"{b:.2f}\", color=marker_color, fontsize=10, ha=\"center\", va=\"bottom\")\n",
    "\n",
    "    # High vs Mid\n",
    "    b, lo, hi = coef_dict[\"high\"]\n",
    "    ax.errorbar(b, y_high, xerr=[[b-lo],[hi-b]], fmt=\"o\", color=marker_color, ecolor=ci_color,\n",
    "                elinewidth=2.0, capsize=4, markersize=7)\n",
    "    ax.text(b, y_high - 0.06, f\"{b:.2f}\", color=marker_color, fontsize=10, ha=\"center\", va=\"top\")\n",
    "\n",
    "    ax.set_yticks([y_low, y_high])\n",
    "    ax.set_yticklabels([\"Low (vs Mid)\", \"High (vs Mid)\"])\n",
    "    ax.set_ylim(-0.6, 0.6)\n",
    "\n",
    "    ax.set_title(title, pad=12)\n",
    "    ax.set_xlabel(\"Competitiveness × Wealth (vs Middle)\")\n",
    "\n",
    "    for spine in [\"top\", \"right\", \"left\", \"bottom\"]:\n",
    "        ax.spines[spine].set_visible(True)\n",
    "        ax.spines[spine].set_linewidth(1.0)\n",
    "    ax.grid(False)\n",
    "\n",
    "# ----------------------------\n",
    "# Make 2x2 figure\n",
    "# ----------------------------\n",
    "fig, axes = plt.subplots(2, 2, figsize=(11.5, 9.2), dpi=300)\n",
    "\n",
    "plot_pred_panel(axes[0,0], pred_econ, \"(a) Economic improvement expectation (predicted)\")\n",
    "plot_pred_panel(axes[0,1], pred_comp, \"(b) Competitiveness linkage (predicted)\")\n",
    "\n",
    "plot_coef_panel(axes[1,0], coef_econ, \"(c) Economic improvement expectation (coefficients)\")\n",
    "plot_coef_panel(axes[1,1], coef_comp, \"(d) Competitiveness linkage (coefficients)\")\n",
    "\n",
    "# y-label only for top row (shared conceptually)\n",
    "axes[0,0].set_ylabel(\"Agreement (1–7)\")\n",
    "axes[0,1].set_ylabel(\"Agreement (1–7)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(wspace=0.22, hspace=0.35)\n",
    "\n",
    "fig.savefig(OUT_PNG, bbox_inches=\"tight\")\n",
    "fig.savefig(OUT_PDF, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nSaved:\")\n",
    "print(OUT_PNG)\n",
    "print(OUT_PDF)"
   ]
  },
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